Relevant bibliographies by topics / Kalman filter based tracking loop

Academic literature on the topic 'Kalman filter based tracking loop'

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Published: 14 March 2023

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Journal articles on the topic "Kalman filter based tracking loop"

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Klokov, Andrey, Motayam Kanouj, and Aleksandr Mironchev. "A Novel Carrier Tracking Approach for GPS Signals Based on Gauss–Hermite Kalman Filter." Electronics 11, no. 14 (July 15, 2022): 2215. http://dx.doi.org/10.3390/electronics11142215.

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In a conventional GPS receiver, the carrier tracking system is the key stage that keeps the receiver locked to the radio navigation parameters (RNPs) of the received signal. The most commonly used approaches to the tracking system are phase lock loop (PLL), frequency lock loop (FLL), and FLL-assisted PLL. The main limitation of the above approaches is that their performance deteriorates when working with weak signals and in harsh environments. In recent years, Kalman filter (KF)-based tracking loop architectures have gained increased attention due to their robust and better performance compared with conventional architectures. In this paper, a novel Gauss–Hermite Kalman filtering-based carrier tracking algorithm is proposed for static and moving receivers with weak GPS signals. The performance of the proposed algorithm is compared with two other approaches: extended Kalman filter (EKF) and unscented Kalman filter (UKF). Simulations were conducted using a software-defined GPS simulator and software device radio (SDR) modules. A comparative analysis of the tracking methods demonstrated that the proposed tracking method shows a better performance and improves the tracking sensitivity and capability under weak signal conditions as compared with EKF- and UKF-based tracking methods. In addition, the results show that the proposed approach improves the Doppler frequency measurement accuracy under dynamic operation conditions.
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Luo, Yu, Yong-qing Wang, Hai-kun Luo, Yuan-xing Ma, and Si-liang Wu. "Study on Vector Tracking Loop Based on Extended Kalman Filter." Journal of Electronics & Information Technology 35, no. 6 (February 17, 2014): 1400–1405. http://dx.doi.org/10.3724/sp.j.1146.2012.00828.

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Cheng, Yan, Qing Chang, Hao Wang, and Xianxu Li. "A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals." Sensors 19, no. 6 (March 19, 2019): 1369. http://dx.doi.org/10.3390/s19061369.

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For global navigation satellite system receivers, Kalman filter (KF)-based tracking loops show remarkable advantages in terms of tracking sensitivity and robustness compared with conventional tracking loops. However, to improve the tracking sensitivity further, increasing the coherent integration time is necessary, but it is typically limited by the navigation data bit sign transition. Moreover, for standard KF-based tracking receivers, the KF parameters are initialized by the acquired results. However, especially under weak signal conditions, the acquired results have frequency errors that are too large for KF-based tracking to converge rapidly to a steady state. To solve these problems, a two-stage KF-based tracking architecture is proposed to track weaker signals and achieve faster convergence. In the first stage, coarse tracking refines the acquired results and achieves bit synchronization. Then, in the second stage, fine tracking initializes the KF-based tracking by using the coarse tracking results and extends the coherent integration time without the bit sign transition limitation. This architecture not only utilizes the self-tuning technique of the KF to improve the tracking sensitivity, but also adopts the two-stage to reduce the convergence time of the KF-based tracking. Simulation results demonstrate that the proposed method outperforms conventional tracking techniques in terms of tracking sensitivity. Furthermore, the proposed method is compared with the standard KF-based tracking approach, proving that the proposed method converges more rapidly.
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Ding, Ji Cheng, Lin Zhao, Jia Liu, and Shuai He Gao. "An Array Nonlinear Kalman Tracker for Indoor GPS Signal." Applied Mechanics and Materials 44-47 (December 2010): 3864–68. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3864.

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To implement indoor GPS signal tracking in standalone mode when the tracking loop is unlocked and data bit edge is unknown, the paper develops a modified Viterbi Algorithm (MVA) based on dynamic programming, and it was applied for GPS bit synchronization. Besides, two combination carrier tracking schemes based on Central Difference Kalman Filter (CDKF) and MVA module were designed for indoor GPS signal. The testing results indicate that the methods can successful detect bit edge position with high detection probability whether or not the tracking loop is locked. The co-operational tracking scheme is still able to perform when the signal quality deteriorate.
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Zou, Xiaojun, Baowang Lian, and Zesheng Dan. "Vector Tracking Algorithm Based on Adaptive Cubature Kalman Filter." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 6 (December 2018): 1108–15. http://dx.doi.org/10.1051/jnwpu/20183661108.

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In the vector tracking loop, there is a great error in the output of discriminator owing to the disturbance of noise. Cubature Kalman filter is proposed to replace the discriminator to process I/Q data and generate code phase error and the carrier frequency error in this paper. The present algorithm not only can avoid the nonlinear problem of discriminator, but also can reduce the bad effect of noise. Moreover, using cubature Kalman filter to deal with the nonlinear I/Q data is beneficial to preserve the accuracy of data processing. Because noise is unknown or time-varying, the filter should have the ability to respond to the changes of environmental noise. The innovation of measurements is used to estimate the covariance matrix of measurement noise in real time. Finally, a comparison is carried out between the present algorithm and the vector tracking algorithm based on discriminator. The test results show that the code phase error and the carrier frequency error are smaller, and the accuracy of navigation solution is also higher.
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Zhao, Sihao, Mingquan Lu, and Zhenming Feng. "Implementation and Performance Assessment of a Vector Tracking Method Based on a Software GPS Receiver." Journal of Navigation 64, S1 (October 14, 2011): S151—S161. http://dx.doi.org/10.1017/s0373463311000440.

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A number of methods have been developed to enhance the robustness of Global Positioning System (GPS) receivers when there are a limited number of visible satellites. Vector tracking is one of them. It utilizes information from all channels to aid the processing of individual channels to generate receiver positions and velocities. This paper analyzes relationships among code phase, carrier frequency, and receiver position and velocity, and presents a vector loop-tracking algorithm using an Extended Kalman filter implemented in a Matlab-based GPS software receiver. Simulated GPS signals are generated to test the proposed vector tracking method. The results show that when some of the satellites are blocked, the vector tracking loop provides better carrier frequency tracking results for the blocked signals and produces more accurate navigation solutions compared with traditional scalar tracking loops.
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Yuan, Cao, Ma Lianchuan, and Weigang Ma. "Mobile Target Tracking Based on Hybrid Open-Loop Monocular Vision Motion Control Strategy." Discrete Dynamics in Nature and Society 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/690576.

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This paper proposes a new real-time target tracking method based on the open-loop monocular vision motion control. It uses the particle filter technique to predict the moving target’s position in an image. Due to the properties of the particle filter, the method can effectively master the motion behaviors of the linear and nonlinear. In addition, the method uses the simple mathematical operation to transfer the image information in the mobile target to its real coordinate information. Therefore, it requires few operating resources. Moreover, the method adopts the monocular vision approach, which is a single camera, to achieve its objective by using few hardware resources. Firstly, the method evaluates the next time’s position and size of the target in an image. Later, the real position of the objective corresponding to the obtained information is predicted. At last, the mobile robot should be controlled in the center of the camera’s vision. The paper conducts the tracking test to the L-type and the S-type and compares with the Kalman filtering method. The experimental results show that the method achieves a better tracking effect in the L-shape experiment, and its effect is superior to the Kalman filter technique in the L-type or S-type tracking experiment.
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Chen, Shaohua, and Yang Gao. "Improvement of Carrier Phase Tracking Based on a Joint Vector Architecture." International Journal of Aerospace Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9682875.

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Carrier phase measurements are essential to high precision positioning. Usually, the carrier phase measurements are generated from the phase lock loop in a conventional Global Navigation Satellite System (GNSS) receiver. However there is a dilemma problem to the design of the loop parameters in a conventional tracking loop. To address this problem and improve the carrier phase tracking sensitivity, a carrier phase tracking method based on a joint vector architecture is proposed. The joint vector architecture contains a common loop based on extended Kalman filter to track the common dynamics of the different channels and the individual loops for each channel to track the satellite specific dynamics. The transfer function model of the proposed architecture is derived. The proposed method and the conventional scalar carrier phase tracking are tested with a high quality simulator. The test results indicate that carrier phase measurements of satellites start to show cycle slips using the proposed method when carrier noise ratio is equal to and below 15 dB-Hz instead of 21 dB-Hz with using the conventional phase tracking loop. Since the joint vector based tracking loops jointly process the signals of all available satellites, the potential interchannel influence between different satellites is also investigated.
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Li, Na, Shufang Zhang, and Yi Jiang. "High Dynamic Weak Signal Tracking Algorithm of a Beidou Vector Receiver Based on an Adaptive Square Root Cubature Kalman Filter." Sensors 21, no. 20 (October 9, 2021): 6707. http://dx.doi.org/10.3390/s21206707.

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Compared with a scalar tracking receiver, the Beidou vector tracking receiver has the advantages of smaller tracking errors, fast loss-of-lock reacquisition, and high stability. However, in extremely challenging conditions, such as highly dynamic and weak signals, the loop will exhibit a high degree of nonlinearity, and observations with gross errors and large deviations will reduce the positioning accuracy and stability. In view of this situation, based on the concepts of cubature Kalman filtering and square root filtering, a square root cubature Kalman filtering (SRCKF) algorithm is given. Then, combining this algorithm with the concept of covariance matching based on an innovation sequence, an adaptive square root cubature Kalman filter (ASRCKF) algorithm is proposed. The algorithm was verified, and the tracking performance of the vector locking loop (VLL) realized by the algorithm was compared with the SRCKF VLL and the ASRCKF scalar locking loop (SLL). The simulation results show that, regardless of whether in a highly dynamic weak signal environment or in a general situation where the signal-to-noise ratio is higher than the tracking threshold, the tracking accuracy and stability of the ASRCKF VLL are higher than those of the SRCKF VLL and the ASRCKF SLL, the three-dimensional position error of the ASRCKF VLL does not exceed 36 m, and the three-dimensional velocity error does not exceed 3.5 m/s.
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Luo, Y., C. Yu, J. Li, and N. El-Sheimy. "PERFORMANCE OF GNSS CARRIER-TRACKING LOOP BASED ON KALMAN FILTER IN A CHALLENGING ENVIRONMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1687–93. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1687-2019.

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<p><strong>Abstract.</strong> The global navigation satellite system (GNSS) recently plays an extremely important role in positioning, navigation, and timing (PNT) applications for the modernized automations and mechanizations, e.g., unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), military aircrafts, etc. Nevertheless, GNSS signals are very vulnerable to the influence of various interferences when they are received on Earth, and the reason why it happens is that the long line-of-sight (LOS) distance between the satellite and the receiver user dramatically reduces the power strength after the signal reaches at the ground. The weak GNSS signal is hard to be handled with traditional phase lock loop (PLL), especially in a dynamic environment. Again, the trade-off among the coherent integration time of tracking loop, received signal power strength, and signal or user receiver dynamics is still a tough and remained problem to be solved. The Kalman filter (KF) is always a promising tool to efficiently decrease the random noise for the tracking process. In our work, we evaluate the performances of the tracking loop modelled with both standard KF and extended Kalman filter (EKF). An adaptive algorithm for the covariance matrix of the process noise is contained in our system to increase the tracking ability in a weak and dynamic environment. Besides, a noise channel is also contained to automatically adjust the priori measurement covariance for the KF tracking loop model. Simulation results demonstrate the performance with the proposed technique.</p>
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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, California
FLL’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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Books on the topic "Kalman filter based tracking loop"

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Vaez-Zadeh, Sadegh. Parameter Estimation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.003.0007.

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In this chapter, the estimation of permanent magnetic synchronous (PMS) motor parameters, including stator winding resistance, motor inductances, and magnitude of permanent magnet flux linage, is presented in two main categories, i.e., offline and online. Several offline schemes, including DC and AC standstill tests, no-load test, load test, and vector control schemes, are presented for estimation of all the motor parameters. Major online schemes used in the estimation of PMS motor parameters are also presented in this chapter. They include closed-loop observer-based estimation, model reference adaptive system (MRAS)-based estimation, recursive least-squares (RLS) estimation, and extended Kalman filter scheme. The online schemes take into account the motor parameter variations during motor operation. The motor model, estimation procedure, and the connection of estimation systems to the motor control system are discussed for each parameter estimation scheme.
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Book chapters on the topic "Kalman filter based tracking loop"

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Gu, Mingxing, Yuan Hu, Minghui Mou, Shengzheng Wang, and Wei Liu. "A GNSS Loop Tracking Structure Based on Unscented Kalman Filter." In Lecture Notes in Electrical Engineering, 239–51. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3146-7_23.

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Li, Bing, Wenfei Guo, Xiaoji Niu, Nesreen I. Ziedan, and Jingnan Liu. "Parameters Design Method of Kalman Filter-Based Tracking Loop in GNSS/INS Deep Integration." In China Satellite Navigation Conference (CSNC) 2017 Proceedings: Volume I, 933–43. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4588-2_79.

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Kumar, Nikhil, Meenakshi Massey, and Neeta Kandpal. "Kalman Filter Based Point Target Tracking." In Springer Proceedings in Physics, 373–75. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9259-1_85.

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Cun, Zhang, Zhao Meng, Yu Xue-Lian, Cui Ming-Lei, Zhou Yun, and Wang Xue-Gang. "Cubature Kalman Filter Based on Strong Tracking." In Lecture Notes in Electrical Engineering, 131–38. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08991-1_14.

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Walrand, Jean. "Tracking: B." In Probability in Electrical Engineering and Computer Science, 193–204. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49995-2_10.

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AbstractIn Chapter Tracking: A, we explained the estimation of a random variable based on observations. We also described the Kalman filter and we gave a number of examples. In this chapter, we derive the Kalman filter and explain some of its properties. We also discuss the extended Kalman filter.Section 10.1 explains how to update an estimate as one makes additional observations. Section 10.2 derives the Kalman filter. The properties of the Kalman filter are explained in Sect. 10.3. Section 10.4 shows how the Kalman filter is extended to nonlinear systems.
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He, Futong, Jiaqi Zhen, and Zhifang Wang. "Video Target Tracking Based on Adaptive Kalman Filter." In Lecture Notes in Electrical Engineering, 1198–201. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_141.

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Kim, Gyuyeong, Hyuntae Kim, Jangsik Park, and Yunsik Yu. "Vehicle Tracking Based on Kalman Filter in Tunnel." In Communications in Computer and Information Science, 250–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23141-4_24.

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Zhou, Xingyu, Dongmei Fu, Yanan Shi, and Chunhong Wu. "Adaptive Learning Compressive Tracking Based on Kalman Filter." In Lecture Notes in Computer Science, 243–53. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71598-8_22.

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Ng, Yonhon, Yasir Latif, Tat-Jun Chin, and Robert Mahony. "Asynchronous Kalman Filter for Event-Based Star Tracking." In Lecture Notes in Computer Science, 66–79. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25056-9_5.

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Zhao, Meng, Xue-lian Yu, Ming-lei Cui, Xue-gang Wang, and Jing Wu. "Square Root Unscented Kalman Filter Based on Strong Tracking." In Lecture Notes in Electrical Engineering, 797–804. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08991-1_83.

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Conference papers on the topic "Kalman filter based tracking loop"

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Wang, Jian, Qianhao Liang, Kun Liang, and Wei Shangguan. "A new extended Kalman filter based carrier tracking loop." In 2009 3rd IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE). IEEE, 2009. http://dx.doi.org/10.1109/mape.2009.5355852.

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Wu, Falin, Linjie Yu, Yan Zhao, and Haibo Zhong. "Performance analysis of typical Kalman filter based GPS tracking loop." In 2014 IEEE International Conference on Control Science and Systems Engineering (CCSSE). IEEE, 2014. http://dx.doi.org/10.1109/ccsse.2014.7224498.

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Wentong, Wang, Li Chuanjun, and Wu jiangxiong. "Performance analysis of a novel kalman filter-based signal tracking loop." In the 2nd International Conference. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3141166.3141171.

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Miao, Jianfeng, Yongrong Sun, Jianye Liu, and Wu Chen. "A Kalman Filter Based Tracking Loop in Weak GPS Signal Processing." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.500.

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Lin, Wenhui, Xin Chen, Yuze Wang, and Di He. "The Design of Asynchronous Kalman Filter-based Tracking Loop in Digital Domain." In 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017). Institute of Navigation, 2017. http://dx.doi.org/10.33012/2017.15238.

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Tang, Xinhua, Gianluca Falco, Emanuela Falletti, and Letizia Lo Presti. "Practical implementation and performance assessment of an Extended Kalman Filter-based signal tracking loop." In 2013 International Conference on Localization and GNSS (ICL-GNSS). IEEE, 2013. http://dx.doi.org/10.1109/icl-gnss.2013.6577275.

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Shen, Jiannan, Xiaowei Cui, Yonghui Zhu, and Mingquan Lu. "A Frequency-Compensation Algorithm in Kalman Filter-Based Tracking Loop for High-Dynamics GNSS Receivers." In 2016 International Technical Meeting of The Institute of Navigation. Institute of Navigation, 2016. http://dx.doi.org/10.33012/2016.13442.

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Tu, Zhiyong, Tiejun Lu, and Qiliang Chen. "A Novel Carrier Loop Based on Unscented Kalman Filter Methods for Tracking High Dynamic GPS Signals." In 2018 IEEE 18th International Conference on Communication Technology (ICCT). IEEE, 2018. http://dx.doi.org/10.1109/icct.2018.8600110.

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Yan, Zhe, Xiyuan Chen, Xinhua Tang, and Xuefen Zhu. "The Implementation and Comparison Between Kalman Filter-based and Vector Tracking Loops." In 2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). IEEE, 2020. http://dx.doi.org/10.1109/icsmd50554.2020.9261648.

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10

Roveda, Loris, Federico Vicentini, Nicola Pedrocchi, Lorenzo Molinari Tosatti, and Francesco Braghin. "Development of Impedance Control Based Strategies for Light-Weight Manipulator Applications Involving Compliant Interacting Environments and Compliant Bases." In ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/esda2014-20136.

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Abstract:
The paper defines impedance control based control laws for interaction tasks with environments of unknown geometrical and mechanical properties, both considering manipulators mounted on A) rigid and B) compliant bases. In A) a deformation-tracking strategy allows the control of a desired deformation of the target environment. In B) a force-tracking strategy allows the control of a desired interaction force. In both A) and B) the on-line estimation of the environment stiffness is required. Therefore, an Extended Kalman Filter is defined. In B) the on-line estimation of the robot base position is used as a feedback in the control loop. The compliant base is modelled as a second-order physical system with known parameters (offline identification) and the base position is estimated from the measure of interaction forces. The Extended Kalman Filter, the grounding position estimation and the defined control laws are validated in simulation and with experiments, especially dedicated to an insertion-assembly task with A) time-varing stiffness environment and B) constant stiffness environment.
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