Dissertations / Theses on the topic 'Kalman filter based tracking loop'
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Schrempp, Mark. "Tracking loop design." Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1363.
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Chen, Hao. "Kalman Filter Aided Tracking Loop In GPS Signal Spoofing Detection." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418909647.
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Lashley, Matthew Bevly David M. Hung John Y. "Kalman filter based tracking algorithms for software GPS receivers." Auburn, Ala., 2006. http://repo.lib.auburn.edu/2006%20Fall/Theses/LASHLEY_MATTHEW_34.pdf.
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Weigang, Zhao, Yao Tingyan, Wu Jinpei, and Zhang Qishan. "SOFT SEAMLESS SWITCHING IN DUAL-LOOP DSP-FLL FOR RAPID ACQUISITION AND TRACKING." International Foundation for Telemetering, 2004. http://hdl.handle.net/10150/605319.
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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, CaliforniaFLL’s are extensively used for fast carrier synchronization. A common approach to meet the wide acquisition range and sufficiently small tracking error requirements is to adopt the wide or narrow band FLL loop in the acquisition and tracking modes and direct switching the loop. The paper analyze the influence of direct switching on performance, including the narrow band loop convergence, transition time etc. and propose applying the Kalman filtering theory to realize the seamless switching (SS) with time-varying loop gains between the two different loop tracking state. The SS control gains for the high dynamic digital spread spectrum receiver is derived. Simulation results for the SS compared to the direct switching demonstrate the improved performance.
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Kanduri, Srinivasa Rangarajan Mukhesh, and Vinay Kumar Reddy Medapati. "Evaluation of TDOA based Football Player’s Position Tracking Algorithm using Kalman Filter." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16433.
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Time Difference Of Arrival (TDOA) based position tracking technique is one of the pinnacles of sports tracking technology. Using radio frequency com-munication, advanced filtering techniques and various computation methods, the position of a moving player in a virtually created sports arena can be iden-tified using MATLAB. It can also be related to player’s movement in real-time. For football in particular, this acts as a powerful tool for coaches to enhanceteam performance. Football clubs can use the player tracking data to boosttheir own team strengths and gain insight into their competing teams as well. This method helps to improve the success rate of Athletes and clubs by analyz-ing the results, which helps in crafting their tactical and strategic approach to game play. The algorithm can also be used to enhance the viewing experienceof audience in the stadium, as well as broadcast.In this thesis work, a typical football field scenario is assumed and an arrayof base stations (BS) are installed along perimeter of the field equidistantly.The player is attached with a radio transmitter which emits radio frequencythroughout the assigned game time. Using the concept of TDOA, the position estimates of the player are generated and the transmitter is tracked contin-uously by the BS. The position estimates are then fed to the Kalman filter, which filters and smoothens the position estimates of the player between the sample points considered. Different paths of the player as straight line, circu-lar, zig-zag paths in the field are animated and the positions of the player are tracked. Based on the error rate of the player’s estimated position, the perfor-mance of the Kalman filter is evaluated. The Kalman filter’s performance is analyzed by varying the number of sample points.
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Isaac, Benson. "Inverse Kinematics and Extended Kalman Filter based Motion Tracking of Human Limb." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406809906.
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Vadde, Susheel Reddy. "Improving Tissue Elasticity Imaging Using A KALMAN Filter-Based Non-Rigid Motion Tracking Algorithm." Youngstown State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1310141393.
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Velmurugan, Rajbabu. "Implementation Strategies for Particle Filter based Target Tracking." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14611.
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This thesis contributes new algorithms and implementations for particle filter-based target tracking. From an algorithmic perspective, modifications that improve a batch-based acoustic direction-of-arrival (DOA), multi-target, particle filter tracker are presented. The main improvements are reduced execution time and increased robustness to target maneuvers. The key feature of the batch-based tracker is an image template-matching approach that handles data association and clutter in measurements. The particle filter tracker is compared to an extended Kalman filter~(EKF) and a Laplacian filter and is shown to perform better for maneuvering targets. Using an approach similar to the acoustic tracker, a radar range-only tracker is also developed. This includes developing the state update and observation models, and proving observability
for a batch of range measurements.
From an implementation perspective, this thesis provides new low-power and real-time implementations for particle filters. First, to achieve a very low-power implementation, two mixed-mode implementation strategies that use
analog and digital components are developed. The mixed-mode implementations use analog, multiple-input translinear element (MITE) networks to realize nonlinear functions. The power dissipated in the mixed-mode implementation of a particle filter-based, bearings-only tracker is compared to a digital implementation that uses the CORDIC algorithm to realize the nonlinear functions. The mixed-mode method that uses predominantly analog components is shown to provide a factor of twenty improvement in power savings compared to a digital implementation. Next, real-time implementation strategies for the batch-based acoustic DOA tracker are developed. The characteristics of the digital implementation of the tracker are quantified using digital signal processor (DSP) and field-programmable gate array (FPGA) implementations. The FPGA implementation uses a soft-core or hard-core processor to implement the Newton search in the particle proposal stage. A MITE implementation of the nonlinear DOA update function in the tracker is also presented.
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Aparicio, Conrado. "Implementation of a quaternion-based Kalman filter for human body motion tracking using MARG sensors." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sep%5FAparicio.pdf.
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Janga, Satyanarayana Reddy. "A Fast and Robust Image-Based Method for tracking Robot-assisted Needle Placement in Real-time MR Images." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/106.
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This thesis deals with automatic localization and tracking of surgical tools such as needles in Magnetic Resonance Imaging(MRI). The accurate and precise localization of needles is very important for medical interventions such as biopsy, brachytherapy, anaesthesia and many other needle based percutaneous interventions. Needle tracking has to be really precise, because the target may reside adjacent to organs which are sensitive to injury. More over during the needle insertion, Magnetic Resonance Imaging(MRI) scan plane must be aligned such that needle is in the field of view (FOV) for surgeon. Many approaches were proposed for needle tracking and automatic MRI scan plane control over last decade that use external markers, but they are not able to account for possible needle bending. Significant amount of work has already been done by using the image based approaches for needle tracking in Image Guided Therapy (IGT) but the existing approaches for surgical robots under MRI guidance are purely based on imaging information; they are missing the important fact that, a lot of important information (for example, depth of insertion, entry point and angle of insertion) is available from the kinematic model of the robot. The existing approaches are also not considering the fact that the needle insertion results in a time sequence of images. So the information about needle positions from the images seen so far can be used to make an approximate estimate about the needle position in the subsequent images. During the course of this thesis we have investigated an image based approach for needle tracking in real-time MR images that leverages additional information available from robot's kinematics model, supplementing the acquired images. The proposed approach uses Standard Hough Transform(SHT) for needle detection in 2D MR image and uses Kalman Filter for tracking the needle over the sequence of images. We have demonstrated experimental validation of the method on Real MRI data using gel phantom and artificially created test images. The results proved that the proposed method can track the needle tip position with root mean squared error of 1.5 mm for straight needle and 2.5mm for curved needle.
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Green, Mark P. (Mark Peter) 1958. "Extended Kalman filter for integrating tracking data from ground-based radar and airborne global positioning system." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/49627.
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Fan, Zheyu Jerry. "Kalman Filter Based Approach : Real-time Control-based Human Motion Prediction in Teleoperation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189210.
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This work is to investigate the performance of two Kalman Filter Algorithms, namely Linear Kalman Filter and Extended Kalman Filter on control-based human motion prediction in a real-time teleoperation. The Kalman Filter Algorithm has been widely used in research areas of motion tracking and GPS-navigation. However, the potential of human motion prediction by utilizing this algorithm is rarely being mentioned. Combine with the known issue - the delay issue in today’s teleoperation services, the author decided to build a prototype of simple teleoperation model based on the Kalman Filter Algorithm with the aim of eliminated the unsynchronization between the user’s inputs and the visual frames, where all the data were transferred over the network. In the first part of the thesis, two types of Kalman Filter Algorithm are applied on the prototype to predict the movement of the robotic arm based on the user’s motion applied on a Haptic Device. The comparisons in performance among the Kalman Filters have also been focused. In the second part, the thesis focuses on optimizing the motion prediction which based on the results of Kalman filtering by using the smoothing algorithm. The last part of the thesis examines the limitation of the prototype, such as how much the delays are accepted and how fast the movement speed of the Phantom Haptic can be, to still be able to obtain reasonable predations with acceptable error rate. The results show that the Extended Kalman Filter has achieved more advantages in motion prediction than the Linear Kalman Filter during the experiments. The unsynchronization issue has been effectively improved by applying the Kalman Filter Algorithm on both state and measurement models when the latency is set to below 200 milliseconds. The additional smoothing algorithm further increases the accuracy. More important, it also solves shaking issue on the visual frames on robotic arm which is caused by the wavy property of the Kalman Filter Algorithm. Furthermore, the optimization method effectively synchronizes the timing when robotic arm touches the interactable object in the prediction. The method which is utilized in this research can be a good reference for the future researches in control-based human motion tracking and prediction.Detta arbete fokuserar på att undersöka prestandan hos två Kalman Filter Algoritmer, nämligen Linear Kalman Filter och Extended Kalman Filter som används i realtids uppskattningar av kontrollbaserad mänsklig rörelse i teleoperationen. Dessa Kalman Filter Algoritmer har används i stor utsträckning forskningsområden i rörelsespårning och GPS-navigering. Emellertid är potentialen i uppskattning av mänsklig rörelse genom att utnyttja denna algoritm sällan nämnas. Genom att kombinera med det kända problemet – fördröjningsproblem i dagens teleoperation tjänster beslutar författaren att bygga en prototyp av en enkel teleoperation modell vilket är baserad på Kalman Filter algoritmen i syftet att eliminera icke-synkronisering mellan användarens inmatningssignaler och visuella information, där alla data överfördes via nätverket. I den första delen av avhandlingen appliceras både Kalman Filter Algoritmer på prototypen för att uppskatta rörelsen av robotarmen baserat på användarens rörelse som anbringas på en haptik enhet. Jämförelserna i prestandan bland de Kalman Filter Algoritmerna har också fokuserats. I den andra delen fokuserar avhandlingen på att optimera uppskattningar av rörelsen som baserat på resultaten av Kalman-filtrering med hjälp av en utjämningsalgoritm. Den sista delen av avhandlingen undersökes begräsning av prototypen, som till exempel hur mycket fördröjningar accepteras och hur snabbt den haptik enheten kan vara, för att kunna erhålla skäliga uppskattningar med acceptabel felfrekvens. Resultaten visar att den Extended Kalman Filter har bättre prestandan i rörelse uppskattningarna än den Linear Kalman Filter under experimenten. Det icke-synkroniseringsproblemet har förbättrats genom att tillämpa de Kalman Filter Algoritmerna på både statliga och värderingsmodeller när latensen är inställd på under 200 millisekunder. Den extra utjämningsalgoritmen ökar ytterligare noggrannheten. Denna algoritm löser också det skakande problem hos de visuella bilder på robotarmen som orsakas av den vågiga egenskapen hos Kalman Filter Algoritmen. Dessutom effektivt synkroniserar den optimeringsmetoden tidpunkten när robotarmen berör objekten i uppskattningarna. Den metod som används i denna forskning kan vara en god referens för framtida undersökningar i kontrollbaserad rörelse- spåning och uppskattning.
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Khajo, Gabriel. "Region Proposal Based Object Detectors Integrated With an Extended Kalman Filter for a Robust Detect-Tracking Algorithm." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72698.
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In this thesis we present a detect-tracking algorithm (see figure 3.1) that combines the detection robustness of static region proposal based object detectors, like the faster region convolutional neural network (R-CNN) and the region-based fully convolutional networks (R-FCN) model, with the tracking prediction strength of extended Kalman filters, by using, what we have called, a translating and non-rigid user input region of interest (RoI-) mapping. This so-called RoI-mapping maps a region, which includes the object that one is interested in tracking, to a featureless three-channeled image. The detection part of our proposed algorithm is then performed on the image that includes only the RoI features (see figure 3.2). After the detection step, our model re-maps the RoI features to the original frame, and translates the RoI to the center of the prediction. If no prediction occurs, our proposed model integrates a temporal dependence through a Kalman filter as a predictor; this filter is continuously corrected when detections do occur. To train the region proposal based object detectors that we integrate into our detect-tracking model, we used TensorFlow®’s object detection api, with a random search hyperparameter tuning, where we fine-tuned, all models from TensorFlow® slim base network classification checkpoints. The trained region proposal based object detectors used the inception V2 base network for the faster R-CNN model and the R-FCN model, while the inception V3 base network only was applied to the faster R-CNN model. This was made to compare the two base networks and their corresponding affects on the detection models. In addition to the deep learning part of this thesis, for the implementation part of our detect-tracking model, like for the extended Kalman filter, we used Python and OpenCV® . The results show that, with a stationary camera reference frame, our proposed detect-tracking algorithm, combined with region proposal based object detectors on images of size 414 × 740 × 3, can detect and track a small object in real-time, like a tennis ball, moving along a horizontal trajectory with an average velocity v ≈ 50 km/h at a distance d = 25 m, with a combined detect-tracking frequency of about 13 to 14 Hz. The largest measured state error between the actual state and the predicted state from the Kalman filter, at the aforementioned horizontal velocity, have been measured to be a maximum of 10-15 pixels, see table 5.1, but in certain frames where many detections occur this error has been shown to be much smaller (3-5 pixels). Additionally, our combined detect-tracking model has also been shown to be able to handle obstacles and two learnable features that overlap, thanks to the integrated extended Kalman filter. Lastly, our detect-tracking model also was applied on a set of infra-red images, where the goal was to detect and track a moving truck moving along a semi-horizontal path. Our results show that a faster R-CNN inception V2 model was able to extract features from a sequence of infra-red frames, and that our proposed RoI-mapping method worked relatively well at detecting only one truck in a short test-sequence (see figure 5.22).
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Miezal, Markus [Verfasser]. "Models, methods and error source investigation for real-time Kalman filter based inertial human body tracking / Markus Miezal." München : Verlag Dr. Hut, 2021. http://d-nb.info/1232847631/34.
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Oh, Seung-Min. "Nonlinear Estimation for Vision-Based Air-to-Air Tracking." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19882.
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Unmanned aerial vehicles (UAV's) have been the focus of significant research interest in both military and commercial areas since they have a variety of practical applications including reconnaissance, surveillance, target acquisition, search and rescue, patrolling, real-time monitoring, and mapping, to name a few. To increase the autonomy and the capability of these UAV's and thus to reduce the workload of human operators, typical autonomous UAV's are usually equipped with both a navigation system and a tracking system. The navigation system provides high-rate ownship states (typically ownship inertial position, inertial velocity, and attitude) that are directly used in the autopilot system, and the tracking system provides low-rate target tracking states (typically target relative position and velocity with respect to the ownship). Target states in the global frame can be obtained by adding the ownship states and the target tracking states. The data estimated from this combination of the navigation system and the tracking system provide key information for the design of most UAV guidance laws, control command generation, trajectory generation, and path planning.
As a baseline system that estimates ownship states, an integrated navigation system is designed by using an extended Kalman filter (EKF) with sequential measurement updates. In order to effectively fuse various sources of aiding sensor information, the sequential measurement update algorithm is introduced in the design of the integrated navigation system with the objective of being implemented in low-cost autonomous UAV's. Since estimated state accuracy using a low-cost, MEMS-based IMU degrades with time, several absolute (low update rate but bounded error in time) sensors, including the GPS receiver, the magnetometer, and the altimeter, can compensate for time-degrading errors. In this work, the sequential measurement update algorithm in smaller vectors and matrices is capable of providing a convenient framework for fusing the many sources of information in the design of integrated navigation systems. In this framework, several aiding sensor measurements with different size and update rates are easily fused with basic high-rate IMU processing.
In order to provide a new mechanism that estimates ownship states, a new nonlinear filtering framework, called the unscented Kalman filter (UKF) with sequential measurement updates, is developed and applied to the design of a new integrated navigation system. The UKF is known to be more accurate and convenient to use with a slightly higher computational cost. This filter provides at least second-order accuracy by approximating Gaussian distributions rather than arbitrary nonlinear functions. This is compared to the first-order accuracy of the well-known EKF based on linearization. In addition, the step of computing the often troublesome Jacobian matrices, always required in the design of an integrated navigation system using the EKF, is eliminated. Furthermore, by employing the concept of sequential measurement updates in the UKF, we can add the advantages of sequential measurement update strategy such as easy compensation of sensor latency, easy fusion of multi-sensors, and easy addition and subtraction of new sensors while maintaining those of the standard UKF such as accurate estimation and removal of Jacobian matrices. Simulation results show better performance of the UKF-based navigation system than the EKF-based system since the UKF-based system is more robust to initial accelerometer and rate gyro biases and more accurate in terms of reducing transient peaks and steady-state errors in ownship state estimation.
In order to estimate target tracking states or target kinematics, a new vision-based tracking system is designed by using a UKF in the scenario of three-dimensional air-to-air tracking. The tracking system can estimate not only the target tracking states but also several target characteristics including target size and acceleration. By introducing the UKF, the new vision-based tracking system presents good estimation performance by overcoming the highly nonlinear characteristics of the problem with a relatively simplified formulation. Moreover, the computational step of messy Jacobian matrices involved in the target acceleration dynamics and angular measurements is removed.
A new particle filtering framework, called an extended marginalized particle filter (EMPF), is developed and applied to the design of a new vision-based tracking system. In this work, only three position components with vision measurements are solved in particle filtering part by applying Rao-Blackwellization or marginalization approach, and the other dynamics, including the target nonlinear acceleration model, with Gaussian noise are effectively handled by using the UKF. Since vision information can be better represented by probabilistic measurements and the EMPF framework can be easily extended to handle this type of measurements, better performance in estimating target tracking states will be achieved by directly incorporating non-Gaussian, probabilistic vision information as the measurement inputs to the vision-based tracking system in the EMPF framework.
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Wang, Weiqi. "Towards real-time tissue surface tracking with a surface-based extended kalman filter for robotic-assisted minimally invasive surgery." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46992.
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The use of registered intra-operative to pre-operative imaging has been proposed for many medical interventions, with the goal of providing more informed guidance to the physician. The registration may be difficult to carry out in real-time.Therefore, it is often necessary to track the motion of the anatomy of interest in order to maintain a registration.
In this work, a surface based Extended Kalman Filter (EKF) framework is proposed to track a tissue surface based on temporal correspondences of 3D features extracted from the tissue surface. Specifically, an initial 3D surface feature map is generated based on stereo matched Scale Invariant Feature Transform (SIFT) feature pairs extracted from the targeted surface. For each consecutive frame, the proposed EKF framework is used to provide 2D temporal matching guidance in both stereo channels for each feature in the surface map. The 2D feature matching is carried out based on the Binary Robust Independent Elementary Feature (BRIEF) descriptor. If the temporal match is successful in both stereo channels, the stereo feature pair can be used to reconstruct the feature location in 3D. The newly measured 3D locations drive the EKF update to simultaneously estimate the current camera motion states and the feature locations of the 3D surface map. The framework is validated on ex vivo porcine tissue surface and in vivo prostate surface during a da Vinci radical prostatectomy. The peak and mean fiducial errors are 2.5 mm and 1.6 mm respectively.
Compared to other methods, the surface based EKF framework can provide a reliable 2D feature matching guidance for each feature in the 3D surface map. This maintains a chance to relocate a feature that was lost for a significant period of time. Such a surface based framework provides persistent feature tracking over time, which is crucial to drift free surface tracking. With implementation on a Graphic Unit Processor (GPU), real time performance is achieved.
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Oksar, Yesim. "Target Tracking With Correlated Measurement Noise." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608198/index.pdf.
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A white Gaussian noise measurement model is widely used in target tracking problem formulation. In practice, the measurement noise may not be white. This phenomenon is due to the scintillation of the target. In many radar systems, the measurement frequency is high enough so that the correlation cannot be ignored without degrading tracking performance.
In this thesis, target tracking problem with correlated measurement noise is considered. The correlated measurement noise is modeled by a first-order Markov model. The effect of correlation is thought as interference, and Optimum Decoding Based Smoothing Algorithm is applied. For linear models, the estimation performances of Optimum Decoding Based Smoothing Algorithm are compared with the performances of Alpha-Beta Filter Algorithm. For nonlinear models, the estimation performances of Optimum Decoding Based Smoothing Algorithm are compared with the performances of Extended Kalman Filter by performing various simulations.
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Zuo, Tianyu. "An Efficient Vision-Based Pedestrian Detection and Tracking System for ITS Applications." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31778.
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In this thesis, a novel Pedestrian Protection System (PPS), composed of the Pedestrian Detection System (PDS) and the Pedestrian Tracking System (PTS), was proposed. The PPS is a supplementary application for the Advanced Driver Assistance System, which is used to avoid collisions between vehicles and pedestrians.
The Pedestrian Detection System (PDS) is used to detect pedestrians from near to
far ranges with the feature-classi er-based detection method (HOG + SVM). To achieve pedestrian detection from near to far ranges, a novel structure was proposed. The structure of our PDS consists of two cameras (called CS and CL separately). The CS is equipped with a short focal length lens to detect pedestrians in near-to-mid range; and, the CL is equipped with a long focal length lens to detect pedestrians in mid-to-far range. To accelerate the processing speed of pedestrian detection, the parallel computing capacity of GPU was utilized in the PDS. The synchronization algorithm is also introduced to synchronize the detection results of CS and CL. Based on the novel pedestrian detection structure, the detection process can reach a distance which is more than 130 meters away without decreasing detection accuracy. The detection range can be extended more than
100 meters without decreasing the processing speed of pedestrian detection. Afterwards, an algorithm to eliminate duplicate detection results is proposed to improve the detection accuracy.
The Pedestrian Tracking System (PTS) is applied following the Pedestrian Detection
System. The PTS is used to track the movement trajectory of pedestrians and to predict the future motion and movement direction. A C + + class (called pedestrianTracking class, which is short for PTC) was generated to operate the tracking process for every detected pedestrian. The Kalman lter is the main algorithm inside the PTC. During the operation of PPS, the nal detection results of each frame from PDS will be transmitted to the PTS to enable the tracking process. The new detection results will be used to update the existing tracking results in the PTS. Moreover, if there is a newly detected pedestrian, a new process will be generated to track the pedestrian in the PTS. Based on the tracking results in PTS, the movement trajectory of pedestrians can be obtained and their future motion and movement direction can be predicted. Two kinds of alerts are generated based on the predictions: warning alert and dangerous alert. These two alerts represent di erent situations; and, they will alert drivers to the upcoming situations. Based on the predictions and alerts, the collisions can be prevented e ectively. The safety
of pedestrians can be guaranteed.
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Del, Favero Simone. "Analysis and Development of Consensus-based Estimation Schemes." Doctoral thesis, Università degli studi di Padova, 2010. http://hdl.handle.net/11577/3427027.
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In the last few decades we assisted to an extraordinary expansion of the Internet and of wireless technologies. These interconnection technologies allow a continuously increasing number of devices to exchange information. This fact, together with the parallel increase in the availability of inexpensive nodes carrying a wide range of sensing capabilities, attracts the interest in developing
large-scale sensing platforms, which could be used to measure a variety of physical phenomena.
However, these huge networks of simple devices are subject to tight energy and bandwidth constraints, making efficient distributed estimation and data fusion algorithms a strong need, to avoid unmanageable computational and communicational burden on network bottleneck nodes.
%In this thesis we address some issues in this research avenue
In this thesis we address some issues in this field, presenting and analyzing distributed algorithms to solve specific distributed estimation problems and carrying out the analysis of some other recently-proposed algorithms.
To perform data fusion in a distributed fashion, we relay on consensus algorithms, namely algorithms that achieve agreement on a common value in the network.
Using consensus as a basic brick to build estimation algorithms we can take advantage of the solid understanding on this problem that many recent contributions deepened and sharpened, and we can leverage for our analysis on powerful and effective tools.
In the thesis we propose a distributed algorithm for offset removal and an algorithm for least-square identification of the wireless-channel parameters, motivated by the application of localization and tracking of a moving object. We present moreover a novel linear algebra inequality, useful in the analysis of randomized algorithms. This result comes into play when we carry out an analysis of a recently-proposed distributed Kalman filtering algorithm. Finally, we look at the intriguing set up of a network cooperation to estimate different but correlated quantities, proposing and analyzing a distributed algorithm that performs inference over a simple Gauss-Markov random field.Gli ultimi decenni sono stati segnati dallo straordinario sviluppo di Internet e dalla pervasiva diffusione della tecnologia wireless, consentendo ad un numero sempre maggiore di dispositivi di scambiare tra loro informazioni. Questo fatto, assieme alla crescente disponibilità, a prezzi modici, di nodi equipaggiati con un'ampia varietà di dispositivi di misura, rende tecnologicamente concretizzabile l'idea di sviluppare grandi piattaforme di sensing, incaricate di monitorare qualsivoglia grandezza fisica. Tuttavia, queste grandi reti di dispositivi estremamente semplici hanno stringenti vincoli sul consumo energetico e sulla banda di comunicazione, che rendono criticamente necessario lo sviluppo di tecniche efficienti per la stima e la data-fusion, così da evitare carichi computazionali e di comunicazione insostenibili ai colli di bottiglia della rete. Questa tesi si propone di contribuire proprio in questo settore, presentando alcuni algoritmi per la soluzione distribuita di specifici problemi di stima ed analizzando le prestazioni di algoritmi recentemente proposti. Strumento chiave nella decentralizzazione della stima è la teoria del consensus, che propone algoritmi in grado di portare l'intera rete a concordare su una specifica quantità. L'utilizzo di algoritmi di consensus come elemento base nella costruzione di algoritmi di stima ci consente di sfruttare la solida comprensione di questo problema, affinata dai molti risultati recentemente proposti in letteratura, e di sfruttare degli strumenti di analisi ben consolidati. Nella tesi, motivati dal problema della localizzazione e del tracking di un oggetto, proponiamo un algoritmo per la compensazione degli offset ed un algoritmo per la stima ai minimi quadrati dei parametri caratterizzanti il canale wireless. Inoltre presentiamo un nuovo risultato di algebra lineare, utile nell'analisi di algoritmi randomizzati. Questo risultato giocherà un ruolo centrale nell'analisi qui proposta di un algoritmo distribuito per la stima alla Kalman. Infine, consideriamo l'interessante caso di una rete di sensori incaricata di stimare quantità diverse ma tra loro correlate e proponiamo un algoritmo per l'inferenza di un semplice campo di Gauss-Markov.
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Mailhot, Nathaniel. "Pupil Tracking and Control of a Laser Based Power System for a Vision Restoring Retinal Implant." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38709.
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For elderly Canadians, the prevalence of vision impairment caused by degenerative
retinal pathologies, such as age-related macular degeneration and retinitis
pigmentosa, is at an occurrence rate of 14 percent, and on the rise. It has been
shown that visual function can be restored by electrically stimulating intact retinal
tissue with an array of micro-electrodes with suitable signals. Commercial retinal
implants carrying such a micro-electrode array achieve this, but to date must
receive power and data over copper wire cable passing through a permanent surgical
incision in the eye wall (sclera). This project is defined by a collaboration
with iBIONICS, who are developing retinal implants for treatment of such conditions.
iBIONICS has developed the Diamond Eye retinal implant, along with
several technology sub-systems to form a comprehensive and viable medical solution.
Notably, the Diamond Eye system can be powered wirelessly, with no need
for a permanent surgical incision.
The thesis work is focused on the formulation, simulation and hardware demonstration
of a powering system, mounted on glasses frame, for a retinal implant.
The system includes a Micro-Electro-Mechanical System (MEMS) mirror that directs
a laser beam to the implant through the pupil opening. The work presented
here is built on two main components: an iterative predictor-corrector algorithm
(Kalman filter) that estimates pupil coordinates from measurements provided by
an image-based eye tracking algorithm; and an misalignment compensation algorithm
that maps eye pupil coordinates into mirror coordinates, and compensates
for misalignment caused by rigid body motions of the glasses lens mirror and the
MEMS mirror with respect to the eye. Pupil tracker and misalignment compensation
control performance are illustrated through simulated scenarios. The project
also involves the development of a hardware prototype that is used to test algorithms
and related software.
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Huttunen, S. (Sami). "Methods and systems for vision-based proactive applications." Doctoral thesis, Oulun yliopisto, 2011. http://urn.fi/urn:isbn:9789514296536.
Full textAbstract:
Abstract Human-computer interaction (HCI) is an integral part of modern society. Since the number of technical devices around us is increasing, the way of interacting is changing as well. The systems of the future should be proactive, so that they can adapt and adjust to people’s movements and actions without requiring any conscious control. Visual information plays a vital role in this kind of implicit human-computer interaction due to its expressiveness. It is therefore obvious that cameras equipped with computing power and computer vision techniques provide an unobtrusive way of analyzing human intentions. Despite its many advantages, use of computer vision is not always straightforward. Typically, every application sets specific requirements for the methods that can be applied. Given these motivations, this thesis aims to develop new vision-based methods and systems that can be utilized in proactive applications. As a case study, the thesis covers two different proactive computer vision applications. Firstly, an automated system that takes care of both the selection and switching of the video source in a distance education situation is presented. The system is further extended with a pan-tilt-zoom camera system that is designed to track the teacher when s/he walks at the front of the classroom. The second proactive application is targeted at mobile devices. The system presented recognizes landscape scenes which can be utilized in automatic shooting mode selection. Distributed smart cameras have been an active area of research in recent years, and they play an important role in many applications. Most of the research has focused on either the computer vision algorithms or on a specific implementation. There has been less activity on building generic frameworks which allow different algorithms, sensors and distribution methods to be used. In this field, the thesis presents an open and expendable framework for development of distributed sensor networks with an emphasis on peer-to-peer networking. From the methodological point of view, the thesis makes its contribution to the field of multi-object tracking. The method presented utilizes soft assignment to associate the measurements to the objects tracked. In addition, the thesis also presents two different ways of extracting location measurements from images. As a result, the method proposed provides location and trajectories of multiple objects which can be utilized in proactive applicationsTiivistelmä Ihmisen ja eri laitteiden välisellä vuorovaikutuksella on keskeinen osa nyky-yhteiskunnassa. Teknisten laitteiden lisääntymisen myötä vuorovaikutustavat ovat myös muuttumassa. Tulevaisuuden järjestelmien tulisi olla proaktiivisia, jotta ne voisivat sopeutua ihmisten liikkeisiin ja toimintoihin ilman tietoista ohjausta. Ilmaisuvoimansa ansiosta visuaalisella tiedolla on keskeinen rooli tällaisessa epäsuorassa ihminen-tietokone –vuorovaikutuksessa. Tämän vuoksi on selvää, että kamerat yhdessä laskentaresurssien ja konenäkömenetelmien kanssa tarjoavat huomaamattoman tavan ihmisten toiminnan analysointiin. Lukuisista eduistaan huolimatta konenäön soveltaminen ei ole aina suoraviivaista. Yleensä jokainen sovellus asettaa erikoisvaatimuksia käytettäville menetelmille. Tästä syystä väitöskirjassa on päämääränä kehittää uusia kuvatietoon perustuvia menetelmiä ja järjestelmiä, joita voidaan hyödyntää proaktiivisissa sovelluksissa. Tässä väitöskirjassa esitellään kaksi proaktiivista sovellusta, jotka molemmat hyödyntävät tietokonenäköä. Ensimmäinen sovellus on etäopetusjärjestelmä, joka valitsee ja vaihtaa kuvalähteen automaattisesti. Järjestelmään esitellään myös ohjattavaan kameraan perustava laajennus, jonka avulla opettajaa voidaan seurata hänen liikkuessaan eri puolilla luokkahuonetta. Toinen proaktiivisen tekniikan sovellus on tarkoitettu mobiililaitteisiin. Kehitetty järjestelmä kykenee tunnistamaan maisemakuvat, jolloin kameran kuvaustila voidaan asettaa automaattisesti. Monissa sovelluksissa on tarpeen käyttää useampia kameroita. Tämän seurauksena eri puolille ympäristöä sijoitettavat älykkäät kamerat ovat olleet viime vuosina erityisen kiinnostuksen kohteena. Suurin osa kehityksestä on kuitenkin keskittynyt lähinnä eri konenäköalgoritmeihin tai yksittäisiin sovelluksiin. Sen sijaan panostukset yleisiin ja helposti laajennettaviin ratkaisuihin, jotka mahdollistavat erilaisten menetelmien, sensoreiden ja tiedonvälityskanavien käyttämisen, ovat olleet vähäisempiä. Tilanteen parantamiseksi väitöskirjassa esitellään hajautettujen sensoriverkkojen kehitykseen tarkoitettu avoin ja laajennettavissa oleva ohjelmistorunko. Menetelmien osalta tässä väitöskirjassa keskitytään useiden kohteiden seurantaan. Kehitetty seurantamenetelmä yhdistää saadut paikkamittaukset seurattaviin kohteisiin siten, että jokaiselle mittaukselle lasketaan todennäköisyys, jolla se kuuluu jokaiseen yksittäiseen seurattavaan kohteeseen. Seurantaongelman lisäksi työssä esitellään kaksi erilaista tapaa, joilla kohteiden paikka kuvassa voidaan määrittää. Esiteltyä kokonaisuutta voidaan hyödyntää proaktiivisissa sovelluksissa, jotka tarvitsevat usean kohteen paikkatiedon tai kohteiden kulkeman reitin
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Khan, Wasiq. "A Novel Approach for Continuous Speech Tracking and Dynamic Time Warping. Adaptive Framing Based Continuous Speech Similarity Measure and Dynamic Time Warping using Kalman Filter and Dynamic State Model." Thesis, University of Bradford, 2014. http://hdl.handle.net/10454/14802.
Full textAbstract:
Dynamic speech properties such as time warping, silence removal and background noise interference are the most challenging issues in continuous speech signal matching. Among all of them, the time warped speech signal matching is of great interest and has been a tough challenge for the researchers. An adaptive framing based continuous speech tracking and similarity measurement approach is introduced in this work following a comprehensive research conducted in the diverse areas of speech processing. A dynamic state model is introduced based on system of linear motion equations which models the input (test) speech signal frame as a unidirectional moving object along the template speech signal. The most similar corresponding frame position in the template speech is estimated which is fused with a feature based similarity observation and the noise variances using a Kalman filter. The Kalman filter provides the final estimated frame position in the template speech at current time which is further used for prediction of a new frame size for the next step. In addition, a keyword spotting approach is proposed by introducing wavelet decomposition based dynamic noise filter and combination of beliefs. The Dempster’s theory of belief combination is deployed for the first time in relation to keyword spotting task. Performances for both; speech tracking and keyword spotting approaches are evaluated using the statistical metrics and gold standards for the binary classification. Experimental results proved the superiority of the proposed approaches over the existing methods.
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Eng, Tseng Lau. "Quantification of carbon emissions and savings in smart grids." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12569.
Full textAbstract:
In this research, carbon emissions and carbon savings in the smart grid are modelled and quantified. Carbon emissions are defined as the product of the activity (energy) and the corresponding carbon factor. The carbon savings are estimated as the difference between the conventional and improved energy usage multiplied by the corresponding carbon factor. An adaptive seasonal model based on the hyperbolic tangent function (HTF) is developed to define seasonal and daily trends of electricity demand and the resultant carbon emissions. A stochastic model describing profiles of energy usage and carbon emissions for groups of consumers is developed. The flexibility of the HTF for modelling cycles of energy consumption is demonstrated and discussed with several case studies. The analytical description to determine electricity grid carbon intensity in the UK is derived, using the available fuel mix data from the Elexon portal. The uncertain realisation of energy data is forecasted and assimilated using the ensemble Kalman filter (EnKF). The numerical optimisation of carbon emissions and savings in the smart grid is further performed using the ensemble-based Closed-loop Production Optimisation Scheme (EnOpt). The EnOpt involves the optimisation of fuel costs and carbon emissions (maximisation of carbon savings) in the smart grid subject to the operational control constraints. The software codes for the based on the application of EnKF and EnOpt are developed, and the optimisation of energy, cost and emissions is performed. The numerical simulation shows the ability of EnKF in forecasting and assimilating the energy data, and the robustness of the EnOpt in optimising costs and carbon savings. The proposed approach addresses the complexity and diversity of the power grid and may be implemented at the level of the transmission operator in collaboration with the operational wholesale electricity market and distribution network operators. The final stage of work includes the quantification of carbon emissions and savings in demand response (DR) programmes. DR programmes such as Short Term Operating Reserve (STOR), Triad, Fast Reserve, Frequency Control by Demand Management (FCDM) and smart meter roll-out are included, with various types of smart interventions. The DR programmes are modelled with appropriate configurations and assumptions in power plants used in the energy industry. This enables the comparison of emissions between the business-as-usual (BAU) and the smart solutions applied, thus deriving the carbon savings. Several case studies involving the modelling and analysing DR programmes are successfully performed. Thus, the thesis represents novel analytical and numerical techniques applied in the fast-growing UK market of smart energy solutions.
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Wijk, Olle. "Triangulation Based Fusion of Sonar Data with Application in Mobile Robot Mapping and Localization." Doctoral thesis, Stockholm : Tekniska högsk, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3124.
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Chen, Shih-Fan, and 陳時帆. "GPS Receiver Tracking Loop Designs Based on the ANFIS-aided Extended Kalman Filter." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/46473562571796711442.
Full textAbstract:
碩士國立臺灣海洋大學
導航與通訊系
93
Abstract The process of conventional code synchronization uses Delay-Locked Loop（DLL） structure most frequently. The conventional DLL loop uses a discriminator function constructed with a specific combination of its early, prompt, and late correlator to detect code tracking error. Multipath is one of the dominant error sources in GPS positioning. It is well known that conventional DLL tracking loop in GPS receiver would suffer from performance degradation due to multipath. When being serious, GPS receiver tracking loop lose lock that might caused by multipath. Extended Kalman Filter（EKF） can optimally combine multiple correlator branches to brtter estimate code tracking error as well as the other signal paratmeters, and to estimate the multipath components for mitigating the multipath problem. In this paper, an EKF based DLL tracking loop to GPS receiver. The I and Q correlator accumulator outputs are used as measurements for the EKF estimation. The EKF extracts and estimates parameters of the direct satellite signal component from the multipath corrupted signal for receiver tracking loop lose lock problem. Then , to apply Adaptive Network-Based Fuzzy Inference System（ANFIS） aid the EKF for reduce the tracking error. The purpose lies in making tracking error of signal is reduced, and verify the feasibility of this method by imitating the way. Keywords：1.GPS 2.Receiver Tracking Loop 3.Kalman Filter 4.ANFIS 5.Error Compensation
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Lin, Yi-Cheng, and 林易澂. "Design and Analysis of Extended Kalman Filter Based GPS Tracking Loops." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/53612771928132359001.
Full textAbstract:
碩士國立臺灣大學
電機工程學研究所
99
Global Positioning System (GPS) is the most widely used Global Navigation Satellite System (GNSS) nowadays. Advantages such as open system, easy to use, and fast and accurate positioning have made it being applied to engineering surveying, vehicular navigation, personal navigation devices, and many other products and systems. In the mid-90s, the U.S. Federal Communication Committee (FCC) demanded the Emergency-911 (E-911) capability on all new generation mobile phones, which requires a return of user position even indoor. This has opened the research of indoor GPS. The main challenge is to process the weak satellite signals degraded by the attenuation due to buildings. In this thesis, tracking loops for weak GPS signals are investigated, which include the conventional tracking loop and the extended Kalman filter (EKF) based tracking loop. Simulation results show that for normal and weak GPS signals, the EKF-based tracking loop has smaller and identical mean squared code phase tracking error than the conventional tracking loop, and it can handle signal dynamics better too. However, the conventional tracking loop requires less accurate initial carrier frequency estimation than the EKF-based tracking loop. In addition, the arithmetic complexity of the two tracking loops are also analyzed and compared in this thesis, to provide a basic prediction of implementation costs.
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Kuang, Shih-ku, and 廣士谷. "Kalman-Filter Based Player Tracking for Broadcast Tennis Video." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/07698681757629063394.
Full textAbstract:
碩士義守大學
資訊工程學系碩士班
97
For tennis video analysis, the technology of player detection and tracking is a very important issue. We can obtain the important features such as the location of the court, and the position, the trajectory and the velocity of players, etc. By these features, the semantics of tennis video can be further extracted. The necessary processing is to separate the foreground (the players) and background (the court), then the algorithm of player detection and tracking can be developed accordingly. Generally, the players in lower court can be detected easily. Because the camera is close to the players, therefore, in the court view, many features of players can be easily detected. However, to detect the players in upper court is very difficult due to the longer camera distance. Also, there exist many noises in the upper court, such as audience, court wall, line judges, ball boys, channel marks and scoreboard, those which make the detection more difficult. In this thesis, we developed an accurate court border method to reduce the noise interference, and then proposed an adaptive color extraction to detect the player. For player tracking, we developed a Kalman Filter based tracking algorithm to address the difficulty of player detection in upper court. The same idea can be also applied to double players tracking with a small modification. Experimential results show the new method can successfully apply to various tennis video.
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Wang, Ting-Wei, and 汪廷瑋. "Dynamic Object Tracking based on CamShift and Kalman Filter." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/60735071047895855691.
Full textAbstract:
碩士中原大學
電機工程研究所
104
In the thesis project of object tracking, we propose the thesis combining the Cam Shift algorithm with Kalman filter which can provide the position of the target more accurately. The tracking algorithm of Cam Shift can only be used in simple background with few colors. By combining the Kalman filter, we can reduce the interference of the background color and make the tracking more accurate. In this thesis, we follow these steps: First, by converting our target image into the H.S.V. color space, we can get desired color information and adjust the contrast of the hue, saturation and brightness. Then we need an initial point to start our tracking process. The traditional Mean Shift theorem does not allow users to change its window size and may lose its target, so that we calculate the search window size and the position of the mass center based on the Cam Shift algorithm. The process is continued until the desired accuracy is met. Finally, due to the instability of the complex background, we add the Kalman filter to lower the noise and interference during the experiment. After the process, we can achieve higher accuracy. The experiments show that the Cam Shift algorithm with the Kalman filter can reduce the noise and instability much more effectively than using only the former. In this thesis, the contributions of the research are as follows: 1.Kalman filter improves and strengthens the stability of the Cam Shift algorithm execution in complex background. 2.It is difficult to detect the object with various colors using in Cam Shift algorithm. Combining it with the Kalman filter improves the object detection capability and make the result more precise. 3.The object tracking experiment helped us expand the measurement range from single color still object to dynamic background processing.
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Chen, Chien-Chung, and 陳建仲. "The Vector Tracking Loop Design of GPS Receiver Using the Unscented Kalman Filter." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58630400968047624563.
Full textAbstract:
碩士國立臺灣海洋大學
通訊與導航工程系
98
The tracking loops are used by traditional receivers to track the signals broadcast by the GPS satellites. This thesis applies the concept of vector tracking loop to GPS signal tracking design. The conventional vector tracking loop is based on the discriminators of DLL and FLL. For obtaining an improved tracking performance in the GPS receiver, several researchers have investigated the vector tracking loop, such as the vector delay lock loop (VDLL) and the vector frequency lock loop (VFLL). All the satellite signals are tracked and processed by a Kalman filter. The VDLL and VFLL combine the tracking of the different satellite PRN and carrier signals into a single algorithm. This algorithm can operate successfully when the independent parallel tracking loop fails. Furthermore, the VDLL can be implemented as part of an ultra-tightly coupled or a deeply integrated GPS receiver so that if the inertial system degrades or fails, the receiver has the interference benefits of VDLL in all of the tracking channels. Simulation results show that the tracking performance of vector tracking loop based on the local filter demonstrates noticeably improved high dynamic resistance capability than that based on the discriminator. Furthermore, use of the Unscented Kalman filter(UKF) as the local filter outperform that of Extended Kalman filter(EKF).
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Carvalho, Guilherme de Sousa. "Kalman Filter-based Object Tracking Techniques for Indoor Robotic Applications." Master's thesis, 2021. http://hdl.handle.net/10316/98163.
Full textAbstract:
Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e TecnologiaThe improvement of social robots have significantly increased, having in view an intelligent mobile robot system, that must be able to perform basic tasks, without compromising the human environment. Therefore, perception module has to be robust enough in object detection and tracking. Thus, the proposal of this dissertation, aims to integrate a multi-object tracking method in a mobile robotics context, mainly focusing on efficiency and performance, using the YOLOv3 object detector to acquire objects location in the image. This dissertation presents a study and exploitation of the SORT and the Deep-SORT Multi-Object Tracking by Detection methods. Aiming to increase robustness of assigning measurements to existing tracks, are introduced different conjugation of similarity metrics, regarding the data association module. Furthermore, to avoid the association between tracks and measurements of different classes, an object class based constraint is applied. These proposed data association techniques, were incorporated in the SORT and the Deep-SORT methods. The SORT, the Deep-SORT, and proposed data association techniques, were evaluated on the MOT17 training set and on the ISR Tracking Dataset (dataset labeled in this study). Moreover, an experiment for evaluating the performance of each method on a lower frame rate condition was performed, showing a decrease of performance. Nevertheless, experimental results attained without using object detector, shown an improvement of performance, when formulating the association problem with different similarity metrics. Throughout the development of this study, an indoor multi-class tracking dataset was labeled, providing useful conditions to validate the proposed framework. Therefore, a general evaluation of the SORT, the Deep-SORT and proposed data association techniques, using the YOLOv3 object detector, was performed in the referred labeled multi-class dataset.
A melhoria dos robôs sociais tem aumentado significativamente, tendo em vista um sistema robótico móvel "inteligente", que deve ser capaz de executar tarefas básicas, sem comprometer o ambiente humano. Portanto, o módulo de perceção tem de ser suficientemente robustos na deteção e rastreamento de objetos (rastreamento equivale à tradução portuguesa de "tracking"). Portanto, a proposta desta dissertação, pretende integrar um método de rastreamento de múltiplos objetos, num contexto de robótica móvel, focando-se em questões de eficiência e desempenho computacional, utilizando o detetor de objetos YOLOv3 para adquirir a localização de objetos na imagem.Esta dissertação apresenta um estudo e exploração dos métodos de rastreamento por deteção, o SORT e o Deep-SORT. Com o objetivo de reforçar a robustez da atribuição de objetos medidos a objetos rastreados, são introduzidas conjugações diferentes de métricas de similaridade, no módulo de associação de dados. Adicionalmente, para evitar a associação de objetos medidos com objetos rastreados de diferentes classes, é aplicada uma restrição baseada em classes de objetos. Estas técnicas propostas de associação de dados, foram incorporadas nos métodos SORT e Deep-SORT.O SORT, o Deep-SORT, e as técnicas de associação de dados propostas, foram avaliados nos dados de treino do MOT17 e no ISR Tracking Dataset (conjunto de dados etiquetado neste estudo). Foram realizados testes ao desempenho dos métodos em condições de taxa reduzida de imagens, evidenciando uma diminuição do desempenho. Contudo, os resultados experimentais obtidos sem utilizar o detetor de objetos, mostram uma melhoria do desempenho, ao formular o problema de associação com métricas de semelhança diferentes. Ao longo do desenvolvimento deste estudo, um conjunto de dados de ambientes multi-classe e de interiores, foi etiquetado com dados de rastreio, fornecendo condições úteis para validar a estrutura proposta. Consequentemente foi realizada uma avaliação geral dos métodos SORT, Deep-SORT e técnicas de associação propostas, utilizando o detetor de objetos YOLOv3, no referido conjunto de dados de rastreio multi-classe etiquetado.
FCT
Universidade de Coimbra - MATIS-CENTRO-01-0145-FEDER-000014, Portugal
31
WANG, RUI-HAO, and 王瑞皓. "Maneuvering Target Tracking using Unscented Kalman Filter based Interacting Multiple Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/j58ccd.
Full textAbstract:
碩士國立雲林科技大學
電機工程系
106
In this paper, we use an unscented Kalman filter for nonlinear motions in an interacting multiple model algorithm to track maneuvering target. The unsented transform (UT) is a method for calculating the statistics of a random variable which undergoes a nonlinear transformation. Consider propagating a random variable through a nonlinear function, and assume that the nonlinear function has mean and covariance. To calculate the statistics of the nonlinear function, we form a matrix of sampling points with corresponding weights. These sampling points are propagated through the nonlinear function and the mean and covariance are approximated using a weighted sample mean and covariance of the posterior sampling points. Through combining the new mean and covarianc of these new sampling points, the state estimation of the nonlinear system can be obtained to achieve accurate nonlinear maneuvering target tracking. When the measured value of the estimator becomes inaccurate due to the influence of high Gaussian distribution noise, the unscented Kalman filter based interacting multiple model (IMM-UKF) is less affected by noise interference than the Kalman filter based interacting multiple model (IMM-KF). Although IMM-UKF is not comparable to the anti-interference ability of the unscented particle pilter based interacting multiple model (IMM-UPF) for Gaussian noise, IMM-UKF owns streamlined computing architecture and computing process. The completion time of the IMM-UKF is much shorter than that of the IMM-UPF which increases the simulation time due to the number of particles. IMM-UKF is more suitable for applications that do not have sufficient time. According to the simulation results of this paper, the estimated time which spent on the IMM-UKF is abou 35 times faster than that of the IMM-UPF.
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Chang, Shih Chi, and 張世其. "3-D Wideband Signal Tracking based on Predictive Bearing Algorithm with Kalman Filter." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/83182025033796442217.
Full textAbstract:
碩士國立海洋大學
電機工程學系
88
For a long time, Taiwan was located in a frequently earthquakes area. Besides this, it''s also an important pivot not only to defend the safety of Asia-Pacific but also to keep the peace of international merchant vessels and the Strait of Taiwan. It''s of great urgency to track the sources bearing using the methods of such as seismology, sonar and radar.Therefore, this thesis mainly uses passive uniform sensor array to track the multiple sources localization in 3-D space, characterized by range, elevation and azimuth. In the underwater multipaths environment, the signal will encounter the coherent phenomenon. In addition, consider the wideband signal as the objective of the system processing for the purpose of high speed transmission. First, data association and high accuracy are the well known properties of narrowband signal Predictive Angle Tracking(PAT) Algorithm based on Kalman Filter. In the mean time, Spatial Smoothing method served as the prepositive processing can overcome coherent signal and using the limitary condition of minimum length to avoid bearing disjudgement caused by abnormally lager angular innovations when the taregts are at crossing point or near by. On the economic consideration, we take the model of Uniform Linear Array(ULA) to estimate accurately the position of the sources in 3-D space and generalize the concept of narrowband PAT algorithm to the conventional Coherent Signal Subspace(CSS) algorithm which is modified as adaptive wideband sources angle tracking algorithm. Finally, we can verify the feasibility and the performance of 3-D space bearing tracking of adaptive wideband sources by using computer simulation.
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CHUNG, CHI-CHIANG, and 鍾志強. "Geometric Unknown Turn Rate Estimation for Maneuvering Target Tracking Based on Kalman Filter." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/z3g384.
Full textAbstract:
碩士國立雲林科技大學
電機工程系
107
This paper presents a simple and effective unknown turn rate estimation method formaneuvering target tracking. The inner product method is first used to find out the angle between the velocity vectors of two consecutive time points. This two velocity vectors does not need to be constant speed. The geometric proof shows that this angle is therotation angle of the target between two consecutive time points,so then the unknown turn rate of the target can be calculated directly through it. Besides, whether the heading direction is left or right can be simply figured out by the twovelocityvectors.A simple Kalman filter (KF) based simulation model is built to demonstrate the robustness of the proposed unknown turn rate estimation method.Simulation results are compared toresult of Multi-modelEstimator (IMM) and the Kalman Filter coordinated turn (CT) model with prior known turn rate to show the effectiveness of the proposed method. We tried to find out the limitation of best conditions and the worst cases by changing the measurement noise and different turn rates, then the 100 times Monte Carlo results of root-mean-square errors(RMSE) of each parameter at every time point is showed.Also, the measurement value is one of the check standards.We can see how much the difference magnitude is during the simulation, and we revised the step of correction by adjustment of the weights of the prediction values and measurement values. After numerous examinations, we finally obtain a ranged scale of which it can present the best result of target tracking. The process data is recorded and showed as a figure of trend. Again, the robustness is viewed by how much the method improved, the gap between revised results and measurement values is calculated as a percentage to show clearly how the tracking estimation can work under unknown turn rates situations.
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Wong, Sio-Sun, and 黃少旋. "Multi-target Visual Tracking Based on Segmented Histograms and Kalman Filter with Occlusion Handling." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/72769446842083558704.
Full textAbstract:
碩士國立臺灣大學
電機工程學研究所
98
A multi-target visual tracking approach, which consists of segmented color histograms, position estimation by Kalman filter and occlusion handling, is proposed in this thesis. The moving object regions are extracted by using Su’s Two-Staged Background Subtraction approach. Connected components are located and those of small area are discarded. Color histograms are used as descriptors of an object’s appearance due to their intrinsic benefits of being relatively scale-invariant and posture-invariant. In order to resolve the problem of losing spatial information due to histogram, an object is segmented into the upper and lower parts. Thus, the objects’ appearances are described by six histograms, namely, R, G, B histograms corresponding to the abovementioned two parts. The appearance model is then updated through Exponentially Weighted Moving Average across time. Furthermore, Kalman filter is used for position estimation for each object. We combined the appearance model and position condition to implement the tracking approach. In view of the challenging occlusion problem, a novel approach consists of Merge, Split, Obstructed and Reappeared is proposed. Historical information is utilized such that severe and even full occlusions can be handled. By and large, this approach can handle long, short, full, partial occlusions efficiently for both inter-object occlusion and occlusion by obstructors. Various experiments verify the robustness of our approach.
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Chen, Chun-hua, and 陳俊樺. "Smart Surveillance System Based on High Level Features and Co-tracking by Kalman Filter." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/51900462733041076436.
Full textAbstract:
碩士國立中央大學
電機工程研究所
98
Tracking multiple targets in complex situation is challenging. The difficulties are tackled multiple targets with occlusions, especially when multiple involved targets are grouped, moving together in appearance ,and target is static. These problems are still focused in recent years. In this paper, we present a multiple targets tracking system for the management of occlusion problem. The proposed algorithm introduces a geometric shape co-tracking strategy. It decomposes targets into geometric shapes located on body and head parts based on reasonable target geometry consideration. Features selected from the decomposed geometric shapes then can be used to track targets through intersections such as occlusion. Projection histogram and ellipsoid shape model are adopted to manage decomposed geometric shapes corresponding to each target. Tracking is done through Kalman filtering process with high efficient and low complexity issue. The problem of sleeping object is handled by mean shift with no foreground needed. Experimental results show that the occlusion of grouped targets can be tracked successfully on recent challenging benchmark sequences. The proposed method is also implemented in DM642. The input video is from a camera transfer to DM642, then the input video has been through tracking process and output the video in LCD. To verify the system, a realistic video has been shown also.
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Chen, Yan-Chu, and 陳彥竹. "Tracking Loop Design Using the Two-Stage Extended Kalman Filter for the Ultra-Tightly Coupled GPS/INS Navigation System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/48800488796746941848.
Full textAbstract:
碩士國立臺灣海洋大學
通訊與導航工程學系
100
In this paper, the two-stage Kalman filter (TKF) for the tracking loop design of the GPS receiver for the ultra-tightly coupled (UTC) GPS/INS integration is carried out. The primary advantage of the UTC configuration is a significant reduction of the tracking loops bandwidth, as the Doppler signal derived from INS aids the tracking loop to remove the dynamics from the GPS signals. The UTC structure reveals many benefits, e.g., disturbance and multipath rejection capability, improved tracking capability for dynamic scenarios and weak signals, and reduction of acquisition time. The unknown random biases degrade the tracking performance of the Kalman filter estimation. Multipath effect, ionospheric delay and tropospheric delay are the main contributing sources of bias errors in GPS position determination. In many practical situations, there are unknown random biases in the system model, essentially caused by the multipath, ionospheric delay and tropospheric delay. The GPS tracking loops may lose lock due to the signals being weak, subjected to excessive dynamics or completely blocked. Traditional GPS tracking loop can be replaced by the vector tracking loop (VTC) for solving some of the problems. In high dynamic situations, the Doppler frequency changes faster with time, and it is therefore the carrier tracking loop of GPS receiver requires a wide bandwidth to track a carrier signal in high dynamic situations. As a specific configuration of the VTC, The ultra-tightly coupled (UTC) GPS/INS integrated navigation integrates the signals from the tracking loops of the GPS receiver with the position and velocity obtained from the INS. To treat the biases of the measurement and system models, the TKF is employed to estimate the signal parameters in the GPS receiver’s signal tracking loop. The proposed method demonstrates the superior capability on improvement of the tracking performance as an alternative estimator for dealing with the low quality signal and high dynamic environments for the tracking loop design of the UTC integration system.
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Shen, Yu-Ping, and 沈予平. "Kalman Filter Based Visual Soccer Ball Tracking and Localization for Teen-size Biped Humanoid Robots." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/46783992746288328064.
Full textAbstract:
碩士國立臺灣科技大學
電機工程系
103
This thesis proposes a Kalman filter (KF) based visual soccer ball tracking and localization approach for an autonomous teen-size biped humanoid robot. This approach is capable of dealing with the visual tracking function in the humanoid league of RoboCup 2015. The competition field was covered by a piece of green carpet painted with white lines in former RoboCup humanoid leagues. Moreover, two identical yellow soccer goals and an orange color ball were used. Therefore, color-based ball recognition is practically used in formers competitions because of uses of distinct colors of ball, goal and field lines. Nevertheless, RoboCup 2015 announced a significant alteration in the specification of ball and goal colors. The major colors of the ball and two goals have been altered as a white color which is the same as the field line. It would be a great challenge to the color recognition when compared to former events, and the approaches with only applying color attribute thresholds. To overcome the aforementioned challenge, this work initially filters out the green carpet color with a linear regressive color space model to precisely retrieve all foreground objects such as robots, ball, goals and filed lines. Subsequently, the soccer ball is identified from the retrieved foreground objects. Especially, a KF is used to predict the ball moving trajectory. The predicted ball moving trajectory is further used to identify the ball position of the next image sampling time. According to the predicted ball position, a dynamic region of interest (ROI) can be generated. Its adjustable ROI dimension is formed with respect to the predicted ball image size with multiplying a tolerance factor which depends on the tracking error range. The dynamic ROI is beneficial to significantly reduce the interference of other objects in the field as well as to improve the computational efforts. Furthermore, a floor localization approach is used to transform the pixel coordinate system to the floor coordinate system. Finally, all proposed approaches have been realized and evaluated in this paper. When compared to the system without employing KF; the computational time of ball tracking is reduced 49.03%. The proposed algorithm has keep applied to our humanoid teen size robot, HuroEvolution TN, and it participated in the soccer game of RoboCup 2015 to practically evaluate the performance. As a consequence, the HuroEvolution TN awarded the second place. The results also verified the feasibility of the proposed approach.
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Bondugula, Srikant. "OPTIMAL CONTROL OF PROJECTS BASED ON KALMAN FILTER APPROACH FOR TRACKING & FORECASTING THE PROJECT PERFORMANCE." 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-05-263.
Full textAbstract:
Traditional scheduling tools like Gantt Charts and CPM while useful in planning and execution of complex construction projects with multiple interdependent activities haven?t been of much help in implementing effective control systems for the same projects in case of deviation from their desired or assumed behavior. Further, in case of such deviations project managers in most cases make decisions which might be guided either by the prospects of short term gains or the intension of forcing the project to follow the original schedule or plan, inadvertently increasing the overall project cost.
Many deterministic project control methods have been proposed by various researchers for calculating optimal resource schedules considering the time-cost as well as the time-cost-quality trade-off analysis. But the need is for a project control system which optimizes the effort or cost required for controlling the project by incorporating the stochastic dynamic nature of the construction-production process. Further, such a system must include a method for updating and revising the beliefs or models used for representing the dynamics of the project using the actual progress data of the project. This research develops such an optimal project control method using Kalman Filter forecasting method for updating and using the assumed project dynamics model for forecasting the Estimated Cost at Completion (EAC) and the Estimated Duration at Completion (EDAC) taking into account the inherent uncertainties in the project progress and progress measurements. The controller is then formulated for iteratively calculating the optimal resource allocation schedule that minimizes either the EAC or both the EAC and EDAC together using the evolutionary optimization algorithm Covariance Matrix Adaption Evolution Strategy (CMA-ES). The implementation of the developed framework is used with a hypothetical project and tested for its robustness in updating the assumed initial project dynamics model and yielding the optimal control policy considering some hypothetical cases of uncertainties in the project progress and progress measurements.
Based on the tests and demonstrations firstly it is concluded that a project dynamics model based on the project Gantt chart for spatial interdependencies of sub-tasks with triangular progress rates is a good representation of a typical construction project; and secondly, it is shown that the use of CMA-ES in conjunction with the Kalman Filter estimation and forecasting method provides a robust framework that can be implemented for any kind of complex construction process for yielding the optimal control policies.
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Lin, Zih-Siang, and 林梓翔. "Extended Kalman Filter Based Fuzzy Decentralized Sliding-Mode Trajectory Tracking Control of Quadrotor Unmanned Aerial Vehicles." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/8y4mua.
Full textAbstract:
碩士國立臺灣科技大學
電機工程系
104
Due to advantages and disadvantages of inertia navigation system (INS) and global positioning system (GPS), it is not suitable for the only use of GPS or INS for the measured velocity and position of outdoor QUAV. Furthermore, the computation of the rotation described by Euler angle is inefficient; its description sometimes possesses the singularity problem. Hence, it is not suitable for the embedded single board computer to calculate the corresponding signals using the Euler angle based rotation description. In this thesis, the nonlinear mathematical model of sensors including GPS and INS is first established. Its linearized model around the GPS signal is constructed for the discrete version of extended Kalman filter (EKF). The estimated position and velocity from EKF are employed to correct the position and velocity of INS such that the controller design is more effective. The compared performances between GPS and INS, and GPS-aided INS confirm the effectiveness of the estimated signal through GPS-aided INS. Subsequently, the EKCF-based fuzzy decentralized path tracking control (FDPTC) is applied for the path tracking of an outdoor QUAV. The proposed control does not need the mathematical dynamic model of QUAV, it only needs its input/output data to construct the fuzzy rule table. After that, three factors and the coefficients of sliding surface for each subsystem are tuned to obtain the satisfactory control performance. Finally, the compared experiments of circular path confirm the effectiveness and robustness of the proposed method.
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Chellal, Majd. "Experimental evaluation of Kalman filter based MPPT in grid-connected PV system." Master's thesis, 2022. http://hdl.handle.net/10198/25144.
Full textAbstract:
Mestrado de dupla diplomação com a Ecóle Supérieur en Sciences ApliquéesPhotovoltaic (PV) energy is becoming an important alternative energy source, since it is abundant in nature, non-polluting and requires low maintenance. However, it suffers from low energy conversion efficiency, which can be even lower if the photovoltaic generator does not operate around a so-called Maximum Power Point (MPP). Tracking this point, which changes its location depending on weather conditions, is a very important step in the design of a photovoltaic system. Several techniques have been investigated in the literature in the MPP context. However, some techniques such as the Kalman filter are steel unknown with a lack of information in real test conditions, since their evaluation is limited only in simulation and literature review. This work presents an experimental evaluation of the Kalman filter based on a comparison with two well-known maximum power point tracking (MPPT) algorithms, which are the Perturbation and observation (among the simplest techniques) and the Particle Swarm Optimization (among the most complex techniques). The experimental tests were carried out under real atmospheric conditions, using Matlab/Simulink and the 1103 dSPACE real-time controller board. The results show that the Kalman filter has a higher aptitude to operate closer to the MPP, with a low oscillation in steady-state compared to the other MPPT evaluated in this work. However, the technique’s flaw lies in the shadow situation where it can not differentiate between the local and global optimums, unlike the particle swarm optimization.
A energia fotovoltaica (PV) está a tornar-se uma importante fonte de energia alternativa, uma vez que é abundante na natureza, não poluente, e requer pouca manutenção. No entanto, sofre de uma baixa eficiência de conversão energética, que pode ser ainda mais baixa se o gerador PV não operar em torno do chamado Ponto de Potência Máxima (MPP). O rastreio deste ponto, que muda a sua localização dependendo das condições meteorológicas, é um passo muito importante na concepção de um sistema PV. Várias técnicas têm sido investigadas na literatura no contexto do MPP. No entanto, o desempenho de algumas técnicas, como o filtro Kalman, em condições reais de teste, ainda desconhecido, ou existe pouca informação, uma vez que a sua avaliação é limitada apenas na simulação e revisão da literatura. Este trabalho apresenta uma avaliação experimental do filtro de Kalman com base numa comparação com dois seguidores de ponto de potência máxima (MPPT) bem conhecidos, que são a Perturbação e observação e a Optimização do Enxame de Partículas. Os testes experimentais foram realizados em condições atmosféricas reais, utilizando o Matlab/Simulink e a carta de controlo em tempo real dSPACE. Os resultados mostram que o filtro de Kalman tem uma maior aptidão para operar mais perto do MPP, com uma baixa oscilação em regime permenente, comparativemente com os outros algoritmos MPPT avaliados neste trabalho. No entanto, a desvantagem ocorre aquando da ocorãncia de sombra, onde a técnica não consegue diferenciar entre os óptimos locais e global, ao contrário da optimização do enxame de partículas. Palavras-chave: Fotovoltaico (PV), Seguimento do Ponto de Potência Máxima (MPPT), Perturbação e Observação (PO), Optimização de enxame de partículas (PSO), Filtro de Kalman (KF), Sistema PV ligado à Rede, dSPACE 1103.
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Chen, Yu-Min, and 陳育民. "Tracking and Attitude of Objects by Quaternion Based on Kalman Filter to Build an Attitude and Heading Reference System." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/89738916870349823382.
Full textAbstract:
碩士中原大學
電機工程研究所
105
In this thesis, we propose a scheme of tracking and attitude of objects by quaternion based on Kalman filter to build an attitude and heading reference system. At first the attitude of the object is calculated by the quaternion and the rotation matrix. To achieve the overall stability of the system, through the estimation to perform attitude and tracking applications. The thesis presents the detailed analysis of position tracking based on Inertial measurement unit. The Sparkfun Inertial measurement unit module contains 3-axis gyroscope, 3-axis accelerometer and 3-axis magnetometer. In the second part of our proposal, in order to overcome the low precision, easy divergence and other shortcomings, a Kalman filter algorithm based on quaternion is employed. The establishment of the state equation requires dynamic error and a gyroscope motion, attitude as a state variable. The measurement scheme is built to take the quaternion of attitude matrix among accelerometers, magnetometers and gyroscopes. Third, in order to measurement the object movement, an Attitude and Heading Reference System with Inertial measurement unit is considered to accurately estimate the attitude. Finally, we use the Bluetooth or XBee communication module to receive real-time attitudinal discrimination and trajectory tracking. The results of tests indicate that the accumulated errors are eliminated in static state and the errors in dynamic state are compensated. In this thesis, the contributions of our research are as follows: 1. Security：We present a wearable sensor system for human motion monitoring in recovery for infants at risk of Sleeping position and climbing. 2. Simplification：It mounted the inertial measurement unit on the human body for estimating trajectory tracking and attitude measuring. 3. Characterization：The quaternion based Kalman filter improves and strengthens the stability of the algorithm execution in wearable devices monitoring inertial device for measuring capability. 4. Trending: To fit the requirement of the increased safety knowledge level of people, we propose an algorithm to combine with a wearable device, an appear for sensor technologies and monitoring devices.
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Ko, Tien-Wen, and 柯天文. "Real-Time Face Detection and Tracking Based on Kalman Filter and Color Information on Mobile Devices for Criminal Detection." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/64s7cx.
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Au, Anthea Wain Sy. "RSS-based WLAN Indoor Positioning and Tracking System Using Compressive Sensing and Its Implementation on Mobile Devices." Thesis, 2010. http://hdl.handle.net/1807/25413.
Full textAbstract:
As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.
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Zhou, Y., Qichun Zhang, H. Wang, P. Zhou, and T. Chai. "EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems." 2017. http://hdl.handle.net/10454/17347.
Full textAbstract:
YesIn this paper, a novel control algorithm is presented to enhance the performance of the tracking property for a class of nonlinear and dynamic stochastic systems subjected to non-Gaussian noises. Although the existing standard PI controller can be used to obtain the basic tracking of the systems, the desired tracking performance of the stochastic systems is difficult to achieve due to the random noises. To improve the tracking performance, an enhanced performance loop is constructed using the EKF-based state estimates without changing the existing closed loop with a PI controller. Meanwhile, the gain of the enhanced performance loop can be obtained based upon the entropy optimization of the tracking error. In addition, the stability of the closed loop system is analyzed in the mean-square sense. The simulation results are given to illustrate the effectiveness of the proposed control algorithm.
This work was supported in part by the PNNL Control of Complex Systems Initiative and in part by the National Natural Science Foundation of China under Grants 61621004,61573022 and 61333007.
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Wang, KeSheng. "Approaches to the improvement of order tracking techniques for vibration based diagnostics in rotating machines." Thesis, 2011. http://hdl.handle.net/2263/28747.
Full textAbstract:
Conventional rotating machine vibration monitoring techniques are based on the assumption that changes in the measured structural response are caused by deterioration in the condition of the rotating machine. However, due to variations of the rotational speed, the measured signal may be non-stationary and difficult to interpret. For this reason, the order tracking technique is introduced. One of main advantages of order tracking over traditional vibration monitoring lies in its ability to clearly identify non-stationary vibration data and to a large extent exclude the influences of varying rotational speed. In recent years, different order tracking techniques have been developed. Each of these has their own pros and cons in analyzing rotating machinery vibration signals. In this research, three existing order tracking techniques are extensively investigated and combined to further explore their abilities in the context of condition monitoring. Firstly, computed order tracking is examined. This allows non-stationary effects due to the variation of rotational speed to be largely excluded. However, this technique was developed to deal with the entire raw signal and therefore looses the ability to focus on each individual order of interest. Secondly, Vold-Kalman filter order tracking is considered. It is widely reported that this technique overcomes many of the limitations of other order tracking methods and extracts order signals into the time domain. However because of the adaptive nature of the Vold-Kalman filter, the non-stationary effects due to the rotational speed will remain in the extracted order waveform, which is not ideal for conventional signal processing methods such as Fourier analysis. Yet, the strict mathematical filter (the Vold-Kalman filter is based upon two rigorous mathematical equations, namely the data equation and the structural equation, to realize the filter) gives this technique an excellent ability to focus on the orders of interest. Thirdly, the empirical mode decomposition method is studied. In the literature, this technique is claimed to be an effective diagnostic tool for various kinds of applications including diagnosis of rotating machinery faults. Its unique empirical way of extracting non-stationary and non-linear signals allows it to capture machine fault information which is intractable by other order tracking methods. But since there is no precise mathematical definition for an intrinsic mode function in empirical mode decomposition and – as far as could be ascertained – no published assessment of the relationship between an order and an intrinsic mode function, this technique has not been properly considered by analysts in terms of order tracking. As a result, its abilities have not really been explored in the context of order related vibrations in rotating machinery. In this research, the relationship between an order and an intrinsic mode function is discussed and it is treated as a special kind of order tracking method. In stead of focusing individually on each order tracking technique, the current work synthesizes different order tracking techniques. Through combination, exchange and reconciliation of ideas between these order tracking techniques, three improved order tracking techniques are developed for the purpose of enhancing order tracking analysis in condition monitoring. The techniques are Vold-Kalman filter and computed order tracking (VKC-OT), intrinsic mode function and Vold-Kalman filter order tracking (IVK-OT) and intrinsic cycle re-sampling (ICR). Indeed, these improved approaches contribute to current order tracking practice, by providing new order tracking methods with new capabilities for condition monitoring of systems which are intractable by traditional order tracking methods, or which enhances results obtained by these traditional methods. The work commences with a discussion of the inter-relationship between the order tracking methods which are considered in the thesis, and exposition of the scope of the work and an explanation of the way these independent order tracking techniques are integrated in the thesis. To demonstrate the abilities of the improved order tracking techniques, two simulation models are established. One is a simple single-degree-of-freedom (SDOF) rotor model with which VKC-OT and IVK-OT techniques are demonstrated. The other is a simplified gear mesh model through which the effectiveness of the ICR technique is proved. Finally two experimental set-ups in the Sasol Laboratory for Structural Mechanics at the University of Pretoria are used for demonstrating the improved approaches for real rotating machine signals. One test rig was established to monitor an automotive alternator driven by a variable speed motor. A stator winding inter-turn short was artificially introduced. Advantages of the VKC-OT technique are presented and features clear and clean order components under non-stationary conditions. The diagnostic ability of the IVK-OT technique of further decomposing an intrinsic mode function is also demonstrated via signals from this test rig, so that order signals and vibrations that modulate orders in IMFs can be separated and used for condition monitoring purposes. The second experimental test rig is a transmission gearbox. Artificially damaged gear teeth were introduced. The ICR technique provides a practical alternative tool for fault diagnosis. It proves to be effective in diagnosing damaged gear teeth.Thesis (PhD)--University of Pretoria, 2011.
Mechanical and Aeronautical Engineering
unrestricted
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(8800964), Maria Nieves Brunet Avalos. "Stereo vision-based system for detection, track and capture of intruder flying drones." Thesis, 2020.
Find full textAbstract:
In this thesis, the design and implementation of an autonomous system that will equip a multi-rotor unmanned aerial vehicle (UAV) for visual detection and tracking of other UAVs is presented. The results from detection and tracking are used for real-time motion planning.
The goal is to effectively detect unwanted UAVs, track them and finally capture them with a net. Having a net that traps the UAVs and enables dragging intruders to another location is of great importance, since these could be carrying dangerous loads.
The project consists of three main tasks: object detection using a stereo camera, video tracking using a Kalman filter based algorithm, and lastly executing an optimal flight plan to aim a net at the detected intruder UAV. The computer vision, motion tracking and planning algorithms are implemented as ROS nodes what makes them executable on a reduced size onboard computer that is installed on the aerial vehicle.
Previous work related to this project consists of either a UAV detection system with computationally heavy algorithms or a tracking algorithm that does not include information about the dynamics of the UAVs. For the capture methods, previous ideas do not consider autonomous decisions or an optimized method to guarantee capture. In this thesis, these three aspects are considered to develop a simple solution that can be mounted on any commercially available UAV.