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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Multiple target tracking algorithms"

1

Wu, Jin, Changqing Cao, Yuedong Zhou, Xiaodong Zeng, Zhejun Feng, Qifan Wu, and Ziqiang Huang. "Multiple Ship Tracking in Remote Sensing Images Using Deep Learning." Remote Sensing 13, no. 18 (September 9, 2021): 3601. http://dx.doi.org/10.3390/rs13183601.

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Анотація:
In remote sensing images, small target size and diverse background cause difficulty in locating targets accurately and quickly. To address the lack of accuracy and inefficient real-time performance of existing tracking algorithms, a multi-object tracking (MOT) algorithm for ships using deep learning was proposed in this study. The feature extraction capability of target detectors determines the performance of MOT algorithms. Therefore, you only look once (YOLO)-v3 model, which has better accuracy and speed than other algorithms, was selected as the target detection framework. The high similarity of ship targets will cause poor tracking results; therefore, we used the multiple granularity network (MGN) to extract richer target appearance information to improve the generalization ability of similar images. We compared the proposed algorithm with other state-of-the-art multi-object tracking algorithms. Results show that the tracking accuracy is improved by 2.23%, while the average running speed is close to 21 frames per second, meeting the needs of real-time tracking.
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2

Ding, Ma. "Tracking Target Identification Model Based on Multiple Algorithms." Applied Mechanics and Materials 539 (July 2014): 106–12. http://dx.doi.org/10.4028/www.scientific.net/amm.539.106.

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In view of current situation of bad data synchronization, image blurring and tracking station stability in tracking target identification, a kind of tracking target identification model based on multiple algorithms was put forward, firstly establishing the image degradation model, using the wavelet algorithm for image preprocessing, doing image edge segmentation by using Robert algorithm after pretreatment, then using the maximum variance threshold method for image threshold segmentation, then extracting target features from the segmented image, and finally using the ABS algorithm to finish target tracking. Experiments proved the proposed model practical and effective.
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3

Ling, Jiankun. "Target Tracking Using Kalman Filter Based Algorithms." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012020. http://dx.doi.org/10.1088/1742-6596/2078/1/012020.

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Abstract Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.
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4

Hoang, Le Minh, Aleksandr A. Konovalov, and Dao Van Luc. "Tracking of Maneuvering Targets Using a Variable Structure Multiple Model Algorithm." Journal of the Russian Universities. Radioelectronics 26, no. 3 (July 6, 2023): 77–89. http://dx.doi.org/10.32603/1993-8985-2023-26-3-77-89.

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Анотація:
Introduction. In recent years, much attention has been paid to the development of trajectory filtering methods for tracking maneuvering targets. Multi-model (MM) algorithms are widely used for filtering maneuvering targets. Conventional MM algorithms are characterized by a fixed structure. However, highly maneuvering targets require a sufficiently large set of models covering the entire range of possible maneuvers, although an increase in the number of models cannot ensure an increase in the accuracy of tracking. To overcome these problems, multiple model algorithms with a variable structure (VSMM) were proposed. This article proposes two VSMM algorithms for tracking maritime targets performing a coordinated turn at constant speed. These are algorithms with a variable set of models based on adaptive grid and switching grid methods.Aim. To develop an adaptive trajectory tracking algorithm that uses a constant turn model to track maneuvering surface objects.Materials and methods. The resulting algorithm is based on the theory of grid adaptation in multi-model estimation methods and is used to estimate the components of the coordinate and velocity vectors of surface maneuvering targets. The algorithm efficiency was evaluated using computer statistical modeling in the MATLAB environment.Results. The structure of an adaptive VSMM algorithm was described. Simulations were carried out to confirm the algorithm efficiency. In the considered simulation scenarios, the algorithm produces effective estimates of the coordinate vectors and speed of surface maneuvering targets.Conclusion. Adaptive algorithms improve the efficiency of target tracking in comparison with multi-model algorithms with a fixed structure, at the same time as saving computational resources.
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5

Yuan, Xianghui, Feng Lian, and Chongzhao Han. "Models and Algorithms for Tracking Target with Coordinated Turn Motion." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/649276.

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Анотація:
Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows its effectiveness.
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6

Lei Shundong. "Tracking Target Identification Model Based on Multiple Algorithms." International Journal of Digital Content Technology and its Applications 7, no. 3 (February 15, 2013): 274–83. http://dx.doi.org/10.4156/jdcta.vol7.issue3.35.

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7

Memon, Sufyan, Myungun Kim, and Hungsun Son. "Tracking and Estimation of Multiple Cross-Over Targets in Clutter." Sensors 19, no. 3 (February 12, 2019): 741. http://dx.doi.org/10.3390/s19030741.

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Анотація:
Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations.
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8

Memon, Sufyan Ali, Hungsun Son, Wan-Gu Kim, Abdul Manan Khan, Mohsin Shahzad, and Uzair Khan. "Tracking Multiple Unmanned Aerial Vehicles through Occlusion in Low-Altitude Airspace." Drones 7, no. 4 (March 30, 2023): 241. http://dx.doi.org/10.3390/drones7040241.

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Анотація:
In an intelligent multi-target tracking (MTT) system, the tracking filter cannot track multi-targets significantly through occlusion in a low-altitude airspace. The most challenging issues are the target deformation, target occlusion and targets being concealed by the presence of background clutter. Thus, the true tracks that follow the desired targets are often lost due to the occlusion of uncertain measurements detected by a sensor, such as a motion capture (mocap) sensor. In addition, sensor measurement noise, process noise and clutter measurements degrade the system performance. To avoid track loss, we use the Markov-chain-two (MC2) model that allows the propagation of target existence through the occlusion region. We utilized the MC2 model in linear multi-target tracking based on the integrated probabilistic data association (LMIPDA) and proposed a modified integrated algorithm referred to here as LMIPDA-MC2. We consider a three-dimensional surveillance for tracking occluded targets, such as unmanned aerial vehicles (UAVs) and other autonomous vehicles at low altitude in clutters. We compared the results of the proposed method with existing Markov-chain model based algorithms using Monte Carlo simulations and practical experiments. We also provide track retention and false-track discrimination (FTD) statistics to explain the significance of the LMIPDA-MC2 algorithm.
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9

Chen, Yuntao, Bin Wu, guangzhi Luo, xiaoyan Chen, and junlin Liu. "Multi-target tracking algorithm based on YOLO+DeepSORT." Journal of Physics: Conference Series 2414, no. 1 (December 1, 2022): 012018. http://dx.doi.org/10.1088/1742-6596/2414/1/012018.

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Abstract After several years of development, the multi-target tracking algorithm has significantly transitioned from being researched to being put into practical production and life. The application field of human detection and tracking technology is closely related to our daily life. In order to solve the problems of the background complexity, the diversity of object shapes in the application of multi-target algorithms, and the mutual occlusion between multiple tracking targets and the lost target, this paper improves the DeepSORT target tracking algorithm, uses the improved YOLO network to detect pedestrians, inputs the detection frame to the Kalman filter for prediction output, and then uses the Hungarian algorithm to realize a tracking frame and detection frame of the predicted output. The experimental results show that target tracking accuracy is increased by 4.3%, the running time is the shortest, and the number of successfully tracked targets is relatively high.
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10

Song, Xiyu, Nae Zheng, and Ting Bai. "Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems." International Journal of Antennas and Propagation 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7241281.

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Анотація:
Considering the demands of different location accuracy for multiple targets tracking, performance-driven resource allocation schemes in distributed MIMO radar system are proposed. Restricted by the tracking antenna number, location estimation mean-square error (MSE), and target priorities, an optimization problem of the minimal antenna subsets selection is modeled as a knapsack problem. Then, two operational schemes, modified fair multistart local search (MFMLS) algorithm and modified fair multistart local search with one antenna to all targets (MFMLS_OAT) algorithm, are presented and evaluated. Simulation results indicate that the proposed MFMLS and MFMLS_OAT algorithm outperform the existing algorithms. Moreover, the MFMLS algorithm can distinguish targets with different priorities, while the MFMLS_OAT algorithm can perform the tracking tasks with higher accuracy.
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Дисертації з теми "Multiple target tracking algorithms"

1

Pitre, Ryan. "A Comparison of Multiple-Model Target Tracking Algorithms." ScholarWorks@UNO, 2004. http://louisdl.louislibraries.org/u?/NOD,168.

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Анотація:
Thesis (M.S.)--University of New Orleans, 2004.
Title from electronic submission form. "A thesis ... in partial fulfillment of the requirements for the degree of Master of Science in the Department of Electrical Engineering."--Thesis t.p. Vita. Includes bibliographical references.
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2

Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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3

Naeem, Asad. "Single and multiple target tracking via hybrid mean shift/particle filter algorithms." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/12699/.

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This thesis is concerned with single and multiple target visual tracking algorithms and their application in the real world. While they are both powerful and general, one of the main challenges of tracking using particle filter-based algorithms is to manage the particle spread. Too wide a spread leads to dispersal of particles onto clutter, but limited spread may lead to difficulty when fast-moving objects and/or high-speed camera motion throw trackers away from their target(s). This thesis addresses the particle spread management problem. Three novel tracking algorithms are presented, each of which combines particle filtering and Kernel Mean Shift methods to produce more robust and accurate tracking. The first single target tracking algorithm, the Structured Octal Kernel Filter (SOK), combines Mean Shift (Comaniciu et al 2003) and Condensation (Isard and Blake 1998a). The spread of the particle set is handled by structurally placing the particles around the object, using eight particles arranged to cover the maximum area. Mean Shift is then applied to each particle to seek the global maxima. In effect, SOK uses intelligent switching between Mean Shift and particle filtering based on a confidence level. Though effective, it requires a threshold to be set and performs a somewhat inflexible search. The second single target tracking algorithm, the Kernel Annealed Mean Shift tracker (KAMS), uses an annealed particle filter (Deutscher et al 2000), but introduces a Mean Shift step to control particle spread. As a result, higher accuracy and robustness are achieved using fewer particles and annealing levels. Finally, KAMS is extended to create a multi-object tracking algorithm (MKAMS) by introducing an interaction filter to handle object collisions and occlusions. All three algorithms are compared experimentally with existing single/multiple object tracking algorithms. The evaluation procedure compares competing algorithms' robustness, accuracy and computational cost using both numerical measures and a novel application of McNemar's statistic. Results are presented on a wide variety of artificial and real image sequences.
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4

Hadzagic, Melita. "Comparative analysis of the IMM-JVC and the IMM-JPDA algorithms for multiple-target tracking." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32959.

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Анотація:
When tracking closely maneuvering targets, the critical role is played by both the chosen method of data association and the target-tracking algorithm. Without an effective association, state estimation is at risk. Without an efficient state prediction, the performance of an associator can be degraded. In developing an assignment strategy the crucial issue is whether to assign a track or observation as belonging uniquely to another observation or track, or to allow a track to be associated non-uniquely with multiple candidate observations.
This thesis presents a comparative study of two assignment alternatives, namely the NC (unique association of a measurement to an existing track) and JPDA (nonunique association of a measurement to an existing track) algorithms. These assignment strategies were combined with an Interacting Multiple Model (IMM) positional estimator, which superiority over the other single scan algorithms has been largely documented. The respective tracking performance of the IMM-JVC and EV1M-JPDAF algorithms for multiple target tracking has been evaluated. After a detailed description of the IMM-JVC and IMM-JPDAF formalisms, and the IMM-JPDAF implementation issues, an analysis of the results of NC association compared to JPDA association is presented. Simulation results obtained on two scenarios involving two closely maneuvering aircraft confirm the superiority of the IMM-JVC.
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5

Munir, Arshed. "Manoeuvring target tracking using different forms of the interacting multiple model algorithm." Thesis, University of Sussex, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240430.

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6

Alat, Gokcen. "A Variable Structure - Autonomous - Interacting Multiple Model Ground Target Tracking Algorithm In Dense Clutter." Phd thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615512/index.pdf.

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Tracking of a single ground target using GMTI radar detections is considered. A Variable Structure- Autonomous- Interactive Multiple Model (VS-A-IMM) structure is developed to address challenges of ground target tracking, while maintaining an acceptable level computational complexity at the same time. The following approach is used in this thesis: Use simple tracker structures
incorporate a priori information such as topographic constraints, road maps as much as possible
use enhanced gating techniques to minimize the eect of clutter
develop methods against stop-move motion and hide motion of the target
tackle on-road/o-road transitions and junction crossings
establish measures against non-detections caused by environment. The tracker structure is derived using a composite state estimation set-up that incorporate multi models and MAP and MMSE estimations. The root mean square position and velocity error performances of the VS-A-IMM algorithm are compared with respect to the baseline IMM and the VS-IMM methods found in the literature. It is observed that the newly developed VS-A-IMM algorithm performs better than the baseline methods in realistic conditions such as on-road/o-road transitions, tunnels, stops, junction crossings, non-detections.
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7

Ege, Emre. "A Comparative Study Of Tracking Algorithms In Underwater Environment Using Sonar Simulation." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608866/index.pdf.

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Target tracking is one the most fundamental elements of a radar system. The aim of target tracking is the reliable estimation of a target'
s true state based on a time history of noisy sensor observations. In real life, the sensor data may include substantial noise. This noise can render the raw sensor data unsuitable to be used directly. Instead, we must filter the noise, preferably in an optimal manner. For land, air and surface marine vehicles, very successful filtering methods are developed. However, because of the significant differences in the underwater propagation environment and the associated differences in the corresponding sensors, the successful use of similar principles and techniques in an underwater scenario is still an active topic of research. A comparative study of the effects of the underwater environment on a number of tracking algorithms is the focus of the present thesis. The tracking algorithms inspected are: the Kalman Filter, the Extended Kalman Filter and the Particle Filter. We also investigate in particular the IMM extension to KF and EKF filters. These algorithms are tested under several underwater environment scenarios.
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8

Niedfeldt, Peter C. "Recursive-RANSAC: A Novel Algorithm for Tracking Multiple Targets in Clutter." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4195.

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Анотація:
Multiple target tracking (MTT) is the process of identifying the number of targets present in a surveillance region and the state estimates, or track, of each target. MTT remains a challenging problem due to the NP-hard data association step, where unlabeled measurements are identified as either a measurement of an existing target, a new target, or a spurious measurement called clutter. Existing techniques suffer from at least one of the following drawbacks: divergence in clutter, underlying assumptions on the number of targets, high computational complexity, time-consuming implementation, poor performance at low detection rates, and/or poor track continuity. Our goal is to develop an efficient MTT algorithm that is simple yet effective and that maintains track continuity enabling persistent tracking of an unknown number of targets. A related field to tracking is regression analysis, where the parameters of static signals are estimated from a batch or a sequence of data. The random sample consensus (RANSAC) algorithm was developed to mitigate the effects of spurious measurements, and has since found wide application within the computer vision community due to its robustness and efficiency. The main concept of RANSAC is to form numerous simple hypotheses from a batch of data and identify the hypothesis with the most supporting measurements. Unfortunately, RANSAC is not designed to track multiple targets using sequential measurements.To this end, we have developed the recursive-RANSAC (R-RANSAC) algorithm, which tracks multiple signals in clutter without requiring prior knowledge of the number of existing signals. The basic premise of the R-RANSAC algorithm is to store a set of RANSAC hypotheses between time steps. New measurements are used to either update existing hypotheses or generate new hypotheses using RANSAC. Storing multiple hypotheses enables R-RANSAC to track multiple targets. Good tracks are identified when a sufficient number of measurements support a hypothesis track. The complexity of R-RANSAC is shown to be squared in the number of measurements and stored tracks, and under moderate assumptions R-RANSAC converges in mean to the true states. We apply R-RANSAC to a variety of simulation, camera, and radar tracking examples.
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9

Day, Nathalie Anna. "Significant measurements of a multiple target tracking system utilizing munkre's algorithm as a correlation scheme." Master's thesis, University of Central Florida, 1988. http://digital.library.ucf.edu/cdm/ref/collection/RTD/id/72470.

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Анотація:
University of Central Florida College of Engineering Thesis
This thesis presents and discusses the principles of multiple target tracking. A simulation written in Turbo Pascal provides the results of using a modified version of Munkre's algorithm for correlating targets with observations. The number and types of measurments necessary to obtain acceptable results are examined. The measurements under scrutiny are range, range rate, azimuth angle and elevation angle. A track-while-scan system is assumed and the nearest neighbor correlation scheme as well as rectangular gating are used for association.
M.S.
Masters
Engineering
Engineering
79 p.
vi, 79 leaves, bound : ill. ; 28 cm.
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10

Sahin, Mehmet Alper. "Performance Optimization Of Monopulse Tracking Radar." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605364/index.pdf.

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An analysis and simulation tool is developed for optimizing system parameters of the monopulse target tracking radar and observing effects of the system parameters on the performance of the system over different scenarios. A monopulse tracking radar is modeled for measuring the performance of the radar with given parameters, during the thesis studies. The radar model simulates the operation of a Class IA type monopulse automatic tracking radar, which uses a planar phased array. The interacting multiple model (IMM) estimator with the Probabilistic Data Association (PDA) technique is used as the tracking filter. In addition to modeling of the tracking radar model, an optimization tool is developed to optimize system parameters of this tracking radar model. The optimization tool implements a Genetic Algorithm (GA) belonging to a GA Toolbox distributed by Department of Automatic Control and System Engineering at University of Sheffield. The thesis presents optimization results over some given optimization scenarios and concludes on effect of tracking filter parameters, beamwidth and dwell interval for the confirmed track.
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Книги з теми "Multiple target tracking algorithms"

1

Multiple-target tracking with radar applications. Dedham, MA: Artech House, 1986.

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2

Nassimizadeh, Hamid. Data association and multiple target tracking. Birmingham: University of Birmingham, 1992.

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3

Dunham, Darin T. Tracking multiple targets in cluttered environments with the probabilistic multi-hypothesis tracking filter. Monterey, Calif: Naval Postgraduate School, 1997.

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4

IEE Seminar on Target Tracking: Algorithms and Applications (2006 Birmingham, England). The IEE seminar on target tracking: algorithms and applications: 7-8 March 2006. London: Institution of Electrical Engineers, 2006.

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5

Engineers, Institution of Electrical, and IEE Control & Automation Professional Network., eds. Target tracking 2004: Algorithms and applications, 23-24 March 2004, the University of Sussex, Brighton, UK. London: Institution of Electrical Engineers, 2004.

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6

IEE Professional Network on Concepts for Automation & Control. International seminar: Target tracking, algorithms & applications : Tuesday, 16 October-Wednesday, 17 October 2001 : Conferentiehotel Drienerburght, University of Twente, Enschede, The Netherlands. London?]: Thales, 2001.

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7

Nicklas, Richard B. An application of a Kalman Filter Fixed Interval Smoothing Algorithm to underwater target tracking. Monterey, Calif: Naval Postgraduate School, 1989.

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8

Dubanov, Aleksandr. Computer simulation in pursuit problems. ru: Publishing Center RIOR, 2022. http://dx.doi.org/10.29039/02102-6.

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Анотація:
Currently, computer simulation in virtual reality systems has a special status. In order for a computer model to meet the requirements of the tasks it models, it is necessary that the mathematical apparatus correctly describe the simulated phenomena. In this monograph, the simulation of pursuit problems is carried out. An adaptive modeling of the behavior of both pursuers and targets is carried out. An iterative calculation of the trajectories of the participants in the pursuit problem is carried out. The main attention is paid to the methods of pursuit and parallel rendezvous. These methods are taken as the basis of the study and are modified in the future. The scientific novelty of the study is the iterative calculation of the trajectories of the participants in the pursuit task when moving at a constant speed, while following the predicted trajectories. The predicted trajectories form a one-parameter network of continuous lines of the first order of smoothness. The predicted trajectories are calculated taking into account the restrictions on the curvature of the participant in the pursuit problem. The fact of restrictions on curvature can be interpreted as restrictions on the angular frequency of rotation of the object of the pursuit problem. Also, the novelty is the calculation of the iterative process of group pursuit of multiple targets, when targets are hit simultaneously or at specified intervals. The calculation of the parameters of the network of predicted trajectories is carried out with a curvature variation in order to achieve the desired temporal effect. The work also simulates the adaptive behavior of the pursuer and the target. The principle of behavior can be expressed on the example of a pursuer with a simple phrase: "You go to the left - I go to the left." This happens at each iteration step in terms of choosing the direction of rotation. For the purpose, the principle of adaptive behavior is expressed by the phrase: "You go to the left - I go to the right." The studies, algorithms and models presented in the monograph can be in demand in the design of autonomously controlled unmanned aerial vehicles with elements of artificial intelligence. The task models in the monograph are supplemented with many animated images, where you can see the research process. Also, the tasks have an implementation in a computer mathematics system and can be transferred to virtual reality systems if necessary.
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9

Bayesian Multiple Target Tracking. Artech House Publishers, 2014.

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Stone, Lawrence D., Carl A. Barlow, and Thomas L. Corwin. Bayesian Multiple Target Tracking (Artech House Radar Library). Artech House Publishers, 1999.

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Частини книг з теми "Multiple target tracking algorithms"

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Liu, Weifeng, Zhong Chai, and Chenglin Wen. "A Multiple Shape-Target Tracking Algorithm by Using MCMC Sampling." In Lecture Notes in Computer Science, 563–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31020-1_67.

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Song, Liping, and Hongbing Ji. "Least Squares Interacting Multiple Model Algorithm for Passive Multi-sensor Maneuvering Target Tracking." In Lecture Notes in Computer Science, 479–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_66.

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Hoh, Baik, and Marco Gruteser. "Multiple Target Tracking." In Encyclopedia of GIS, 764. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_850.

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Hoh, Baik, and Marco Gruteser. "Multiple Target Tracking." In Encyclopedia of GIS, 1–2. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_850-2.

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Phalke, Kiran, and Ravindra Hegadi. "Multiple Target Tracking." In Advances in Intelligent Systems and Computing, 579–85. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8633-5_58.

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Hoh, Baik, and Marco Gruteser. "Multiple Target Tracking." In Encyclopedia of GIS, 1412–13. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_850.

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Streit, Roy L. "Multiple Target Tracking." In Poisson Point Processes, 147–78. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6923-1_6.

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Kyriakides, Ioannis, Darryl Morrell, and Antonia Papandreou-Suppappola. "Multiple Target Tracking." In Adaptive High-Resolution Sensor Waveform Design for Tracking, 41–62. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01515-1_5.

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Wu, Weihua, Hemin Sun, Mao Zheng, and Weiping Huang. "Single Target Tracking Algorithms." In Target Tracking with Random Finite Sets, 41–59. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9815-7_2.

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Wang, Xiaoyu, Gang Hua, and Tony X. Han. "Discriminative Multiple Target Tracking." In Machine Learning for Vision-Based Motion Analysis, 145–58. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-057-1_6.

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Тези доповідей конференцій з теми "Multiple target tracking algorithms"

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Noyes, S. P. "Control of false track rate using multiple hypothesis confirmation." In Target Tracking 2004: Algorithms and Applications. IEE, 2004. http://dx.doi.org/10.1049/ic:20040062.

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Arj, M. "Problems of multiple-target tracking in vision-based applications." In Target Tracking 2004: Algorithms and Applications. IEE, 2004. http://dx.doi.org/10.1049/ic:20040064.

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Hue, C. "Tracking multiple targets with particle filtering using multiple receivers." In IEE International Seminar Target Tracking: Algorithms and Applications. IEE, 2001. http://dx.doi.org/10.1049/ic:20010232.

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Allam, S. "Multiple model tracking with intermittent mode observations." In IEE Colloquium. Target Tracking: Algorithms and Applications. IEE, 1999. http://dx.doi.org/10.1049/ic:19990511.

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Vahdati-khajeh, E. "Tracking the maneuvering targets using multiple scan joint probabilistic data association algorithm." In Target Tracking 2004: Algorithms and Applications. IEE, 2004. http://dx.doi.org/10.1049/ic:20040049.

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Karlsson, R. "Monte Carlo data association for multiple target tracking." In IEE International Seminar Target Tracking: Algorithms and Applications. IEE, 2001. http://dx.doi.org/10.1049/ic:20010239.

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Clark, D., I. T. Ruiz, Y. Petillot, and J. Bell. "Multiple target tracking and data association in sonar images." In IEE Seminar on Target Tracking: Algorithms and Applications. IEE, 2006. http://dx.doi.org/10.1049/ic:20060567.

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Judge, I. "RADIX - a solution to multiple sensor data fusion." In IEE International Seminar Target Tracking: Algorithms and Applications. IEE, 2001. http://dx.doi.org/10.1049/ic:20010231.

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Boers, Y. "Multiple model filters for systems with possibly erroneous measurements." In IEE International Seminar Target Tracking: Algorithms and Applications. IEE, 2001. http://dx.doi.org/10.1049/ic:20010236.

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Jaward, M. H., L. Mihaylova, N. Canagarajah, and D. Bull. "A data association algorithm for multiple object tracking in video sequences." In IEE Seminar on Target Tracking: Algorithms and Applications. IEE, 2006. http://dx.doi.org/10.1049/ic:20060565.

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Звіти організацій з теми "Multiple target tracking algorithms"

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Bose, N. K. Multiple Target Tracking: Fast Algorithm for Data Association and State Estimation. Fort Belvoir, VA: Defense Technical Information Center, February 1995. http://dx.doi.org/10.21236/ada300870.

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Kashyap, Rangasami L. Multiple Target Detection and Tracking. Fort Belvoir, VA: Defense Technical Information Center, February 1999. http://dx.doi.org/10.21236/ada363925.

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Lambert, Hendrick C., and Dana Sinno. Bioinspired Resource Management for Multiple-Sensor Target Tracking Systems. Fort Belvoir, VA: Defense Technical Information Center, June 2011. http://dx.doi.org/10.21236/ada544935.

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Kamalvand, Ahmad, Paul MacDonald, and Thai-Duong Tran. Factored Sampling Tracking: Comparison of the Kalman and the Condensation Algorithms for Missile Tracking in a Defense Target Environment. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada430271.

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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.

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Анотація:
To improve traffic performance and safety, the ability to measure traffic accurately and effectively, including motorists and other vulnerable road users, at road intersections is needed. A past study conducted by the Center for Road Safety has demonstrated that it is feasible to detect and track various types of road users using a LiDAR-based system called TScan. This project aimed to progress towards a real-world implementation of TScan by building two trailer-based prototypes with full end-user documentation. The previously developed detection and tracking algorithms have been modified and converted from the research code to its implementational version written in the C++ programming language. Two trailer-based TScan units have been built. The design of the prototype was iterated multiple times to account for component placement, ease of maintenance, etc. The expansion of the TScan system from a one single-sensor unit to multiple units with multiple LiDAR sensors necessitated transforming all the measurements into a common spatial and temporal reference frame. Engineering applications for performing traffic counts, analyzing speeds at intersections, and visualizing pedestrian presence data were developed. The limitations of the existing SSAM for traffic conflicts analysis with computer simulation prompted the research team to develop and implement their own traffic conflicts detection and analysis technique that is applicable to real-world data. Efficient use of the development system requires proper training of its end users. An INDOT-CRS collaborative process was developed and its execution planned to gradually transfer the two TScan prototypes to INDOT’s full control. This period will be also an opportunity for collecting feedback from the end user and making limited modifications to the system and documentation as needed.
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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).
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