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Journal articles on the topic 'Bayesian target tracking'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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1

Vivone, Gemine, Paolo Braca, Karl Granstrom, and Peter Willett. "Multistatic Bayesian extended target tracking." IEEE Transactions on Aerospace and Electronic Systems 52, no. 6 (2016): 2626–43. http://dx.doi.org/10.1109/taes.2016.150724.

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2

Sun, Kang. "New Compound Method for Target Recognition and Tracking." Applied Mechanics and Materials 273 (January 2013): 790–95. http://dx.doi.org/10.4028/www.scientific.net/amm.273.790.

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In this paper, we propose compound target detection and tracking method that combines Bayesian local features classification and global template tracking. During target initialization phase, we convert local features recognition problem into Semi-Naive Bayesian classification theory to avoid computing and matching complex high-dimension descriptor. During tracking, detector hands over tracking task to the template tracker, which imposes temporal continuity constraints across on-line frames in order to increase the robustness and efficiency of the results. In typical application scenarios, once the tracker loses target, it requires the detector for reinitialization. Experiment results confirm the efficiency of our approach at last.

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3

Quan, Hong Wei, Jun Hua Li, and Xiao Juan Zhang. "Joint Target Detection Tracking and Classification Based on Finite-Set Statistics Theory." Applied Mechanics and Materials 668-669 (October 2014): 1072–75. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1072.

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In traditional target tracking methods, the target number, target states and target class can not be estimated in same time. This paper investigated the joint target detection, tracking and classification method which is based on finite-set statistics theory. First, the random set and finite-set statistics theory are introduced for theoretic analysis. Second, the finite-set model for target tracking is given to construct a generalized nonlinear fusion framework. Finally, the finite-set based Bayesian filter is developed to track the targets in surveillance region. By recursively calculating the probabilistic hypothesis density, the target number, target states and target class can be evaluated simultaneously.

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4

Stone, Larry, Roy Streit, Tom Corwin, Kristine Bell, and Fred Daum. "Bayesian multiple target tracking, 2nd edition [Book review]." IEEE Aerospace and Electronic Systems Magazine 29, no. 8 (2014): 23–24. http://dx.doi.org/10.1109/maes.2014.140049.

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5

Li, Xiaohua, Bo Lu, Wasiq Ali, and Haiyan Jin. "Passive Tracking of Multiple Underwater Targets in Incomplete Detection and Clutter Environment." Entropy 23, no. 8 (2021): 1082. http://dx.doi.org/10.3390/e23081082.

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A major advantage of the use of passive sonar in the tracking multiple underwater targets is that they can be kept covert, which reduces the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the complexity of data association between measurements and targets make the problem of underwater passive multiple target tracking challenging. To deal with these problems, the cardinalized probability hypothesis density (CPHD) recursion, which is based on Bayesian information theory, is developed to handle the data association uncertainty, and to acquire existing targets’ numbers and states (e.g., position and velocity). The key idea of the CPHD recursion is to simultaneously estimate the targets’ intensity and the probability distribution of the number of targets. The CPHD recursion is the first moment approximation of the Bayesian multiple targets filter, which avoids the data association procedure between the targets and measurements including clutter. The Bayesian-filter-based extended Kalman filter (EKF) is applied to deal with the nonlinear bearing and Doppler measurements. The experimental results show that the EKF-based CPHD recursion works well in the underwater passive multiple target tracking system in cluttered and noisy environments.

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6

Haoxiang, Dr Wang, and Dr Smys S. "WSN based Improved Bayesian Algorithm Combined with Enhanced Least-Squares Algorithm for Target Localizing and Tracking." IRO Journal on Sustainable Wireless Systems 2, no. 2 (2020): 59–67. http://dx.doi.org/10.36548/jsws.2020.2.001.

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For wireless sensor network (WSN), localization and tracking of targets are implemented extensively by means of traditional tracking algorithms like classical least-square (CLS) algorithm, extended Kalman filter (EKF) and the Bayesian algorithm. For the purpose of tracking and moving target localization of WSN, this paper proposes an improved Bayesian algorithm that combines the principles of least-square algorithm. For forming a matrix of range joint probability and using target predictive location of obtaining a sub-range probability set, an improved Bayesian algorithm is implemented. During the dormant state of the WSN testbed, an automatic update of the range joint probability matrix occurs. Further, the range probability matrix is used for the calculation and normalization of the weight of every individual measurement. Lastly, based on the weighted least-square algorithm, calculation of the target prediction position and its correction value is performed. The accuracy of positioning of the proposed algorithm is improved when compared to variational Bayes expectation maximization (VBEM), dual-factor enhanced VBAKF (EVBAKF), variational Bayesian adaptive Kalman filtering (VBAKF), the fingerprint Kalman filter (FKF), the position Kalman filter (PKF), the weighted K-nearest neighbor (WKNN) and the EKF algorithms with the values of 0.5%, 7%, 14%, 19%, 33% and 35% respectively. Along with this, when compared to Bayesian algorithm, the computation burden is reduced by the proposed algorithm by a factor of over 80%.

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7

Xiong, Wei, Xiangqi Gu, and Yaqi Cui. "Tracking and Data Association Based on Reinforcement Learning." Electronics 12, no. 11 (2023): 2388. http://dx.doi.org/10.3390/electronics12112388.

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Currently, most multi-target data association methods require the assumption that the target motion model is known, but this assumption is clearly not valid in a real environment. In the case of an unknown system model, the influence of environmental clutter and sensor detection errors on the association results should be considered, as well as the occurrence of strong target maneuvers and the sudden appearance of new targets during the association process. To address these problems, this paper designs a target tracking and data association algorithm based on reinforcement learning. First, this algorithm combines the dynamic exploration capability of reinforcement learning and the long-time memory function of LSTM network to design a policy network that predicts the probability of associating a point with its various possible source targets. Then, the Bayesian network and the multi-order least squares curve fitting method are combined to predict the location of target, and the results are fed into the Bayesian recursive function to obtain the reward. Simultaneously, some corresponding mechanisms are proposed for possible problems that interfere with the association process. Finally, the simulation experimental results show that this algorithm associates the results with higher accuracy compared to other algorithms when faced with the above problem.

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8

Lan Jiang, Sumeetpal S. Singh, and Sinan Yildirim. "Bayesian Tracking and Parameter Learning for Non-Linear Multiple Target Tracking Models." IEEE Transactions on Signal Processing 63, no. 21 (2015): 5733–45. http://dx.doi.org/10.1109/tsp.2015.2454474.

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9

Taguchi, Shun, and Kiyosumi Kidono. "Exclusive Association Sampling to Improve Bayesian Multi-Target Tracking." IEEE Access 8 (2020): 193116–27. http://dx.doi.org/10.1109/access.2020.3032692.

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10

Kumar, Pankaj, and Anthony Dick. "Adaptive earth movers distance‐based Bayesian multi‐target tracking." IET Computer Vision 7, no. 4 (2013): 246–57. http://dx.doi.org/10.1049/iet-cvi.2011.0223.

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11

Bruno, M. G. S. "Bayesian Methods for Multiaspect Target Tracking in Image Sequences." IEEE Transactions on Signal Processing 52, no. 7 (2004): 1848–61. http://dx.doi.org/10.1109/tsp.2004.828903.

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12

Morelande, Mark R., Christopher M. Kreucher, and Keith Kastella. "A Bayesian Approach to Multiple Target Detection and Tracking." IEEE Transactions on Signal Processing 55, no. 5 (2007): 1589–604. http://dx.doi.org/10.1109/tsp.2006.889470.

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13

Gonzalez Dondo, Diego, Javier Andres Redolfi, Martin Griffa, Guillermo Max Steiner, and Luis Rafael Canali. "Target Tracking System Using Multiple Cameras and Bayesian Estimation." IEEE Latin America Transactions 14, no. 6 (2016): 2713–18. http://dx.doi.org/10.1109/tla.2016.7555243.

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14

Haug, A. J. "Bayesian estimation for target tracking: part I, general concepts." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 4 (2012): 375–83. http://dx.doi.org/10.1002/wics.1211.

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15

Chen, Xiaobo, Yanjun Wang, Ling Chen, and Jianyu Ji. "Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference." Sensors 20, no. 22 (2020): 6487. http://dx.doi.org/10.3390/s20226487.

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Cooperative target tracking by multiple vehicles connected through inter-vehicle communication is a promising way to improve the estimation of target state. The effectiveness of cooperative tracking closely depends on the accuracy of relative localization between host and cooperative vehicles. However, the localization signal usually provided by the satellite-based navigation system is rather susceptible to dynamic driving environment, thus influencing the effectiveness of cooperative tracking. In order to implement reliable cooperative tracking, especially when the statistical characteristic of the relative localization noise is time-varying and uncertain, this paper presents a recursive Bayesian framework which jointly estimates the state of the target and the cooperative vehicle as well as the localization noise parameter. An online variational Bayesian inference algorithm is further developed to achieve efficient recursive estimate. The simulation results verify that our proposed algorithm can effectively boost the accuracy of target tracking when the localization noise dynamically changes over time.

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16

Ali, Wasiq, Yaan Li, Zhe Chen, Muhammad Asif Zahoor Raja, Nauman Ahmed, and Xiao Chen. "Application of Spherical-Radial Cubature Bayesian Filtering and Smoothing in Bearings Only Passive Target Tracking." Entropy 21, no. 11 (2019): 1088. http://dx.doi.org/10.3390/e21111088.

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In this paper, an application of spherical radial cubature Bayesian filtering and smoothing algorithms is presented to solve a typical underwater bearings only passive target tracking problem effectively. Generally, passive target tracking problems in the ocean environment are represented with the state-space model having linear system dynamics merged with nonlinear passive measurements, and the system is analyzed with nonlinear filtering algorithms. In the present scheme, an application of spherical radial cubature Bayesian filtering and smoothing is efficiently investigated for accurate state estimation of a far-field moving target in complex ocean environments. The nonlinear model of a Kalman filter based on a Spherical Radial Cubature Kalman Filter (SRCKF) and discrete-time Kalman smoother known as a Spherical Radial Cubature Rauch–Tung–Striebel (SRCRTS) smoother are applied for tracking the semi-curved and curved trajectory of a moving object. The worth of spherical radial cubature Bayesian filtering and smoothing algorithms is validated by comparing with a conventional Unscented Kalman Filter (UKF) and an Unscented Rauch–Tung–Striebel (URTS) smoother. Performance analysis of these techniques is performed for white Gaussian measured noise variations, which is a significant factor in passive target tracking, while the Bearings Only Tracking (BOT) technology is used for modeling of a passive target tracking framework. Simulations based experiments are executed for obtaining least Root Mean Square Error (RMSE) among a true and estimated position of a moving target at every time instant in Cartesian coordinates. Numerical results endorsed the validation of SRCKF and SRCRTS smoothers with better convergence and accuracy rates than that of UKF and URTS for each scenario of passive target tracking problem.

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17

Han, Yulan, and Chongzhao Han. "A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/7424538.

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To track multiple extended targets for the nonlinear system, this paper employs the idea of the particle filter to track kinematic states and shape formation of extended targets. First, the Bayesian framework is proposed for multiple extended targets to jointly estimate multiple extended target state and association hypothesis. Furthermore, a joint proposal distribution is defined for the multiple extended target state and association hypothesis. Then, the Bayesian framework of multiple extended target tracking is implemented by the particle filtering which could release the high computational burden caused by the increase in the number of extended targets and measurements. Simulation results show that the proposed multiple extended target particle filter has superior performance in shape estimation and improves the performance of the position estimation in the situation that there are spatially closed extended targets.

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18

Wang, Junxiang, Xin Wang, Yingying Chen, Mengting Yan, and Hua Lan. "Model Adaptive Kalman Filter for Maneuvering Target Tracking Based on Variational Inference." Electronics 14, no. 10 (2025): 1908. https://doi.org/10.3390/electronics14101908.

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This study introduces a new variational Bayesian adaptive estimator that enhances traditional interactive multiple model (IMM) frameworks for maneuvering target tracking. Conventional IMM algorithms struggle with rapid maneuvers due to model-switching delays and fixed structures. Our method uses Bayesian inference to update change-point statistics in real-time for quick model switching. Variational Bayesian inference approximates the complex posterior distribution, transforming target state estimation and model identification into an optimization task to maximize the evidence lower bound (ELBO). A closed-loop iterative mechanism jointly optimizes the target state and model posterior. Experiments in six simulated and two real-world scenarios show our method outperforms current algorithms, especially in high maneuverability contexts.

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19

Zhang, Huanqing, Hongwei Ge, and Jinlong Yang. "Improved Gaussian Mixture Probability Hypothesis Density for Tracking Closely Spaced Targets." International Journal of Electronics and Telecommunications 63, no. 3 (2017): 247–54. http://dx.doi.org/10.1515/eletel-2017-0033.

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AbstractProbability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm.

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20

Fong, Li Wei. "Decoupled Adaptive Tracking Algorithm for Multi-Sensor Measurement Fusion." Applied Mechanics and Materials 229-231 (November 2012): 1235–38. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1235.

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A decoupled adaptive tracking filter is developed for centralized measurement fusion to track the same maneuvering target to improve the tracking accuracy. The proposed approach consists of a dual-band Kalman filter and a two-category Bayesian classifier. Based upon data compression and decoupling techniques, two parallel decoupled filters are obtained for lessening computation. The Bayesian classification scheme is employed which involves switching between high-level-band filter and low-level-band filter to continuously resist different target maneuver turns. The simulation results are presented which demonstrate the effectiveness of the proposed method.

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21

Liu, Jiaqi, Zhen Wang, Di Cheng, Weidong Chen, and Chang Chen. "Marine Extended Target Tracking for Scanning Radar Data Using Correlation Filter and Bayes Filter Jointly." Remote Sensing 14, no. 23 (2022): 5937. http://dx.doi.org/10.3390/rs14235937.

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As the radar resolution improves, the extended structure of the targets in radar echoes can make a significant contribution to improving tracking performance, hence specific trackers need to be designed for these targets. However, traditional radar target tracking methods are mainly based on the accumulation of the target’s motion information, and the target’s appearance information is ignored. In this paper, a novel tracking algorithm that exploits both the appearance and motion information of a target is proposed to track a single extended target in maritime surveillance scenarios by incorporating the Bayesian motion state filter and the correlation appearance filter. The proposed algorithm consists of three modules. Firstly, a Bayesian module is utilized to accumulate the motion information of the target. Secondly, a correlation module is performed to capture the appearance features of the target. Finally, a fusion module is proposed to integrate the results of the former two modules according to the Maximum A Posteriori Criterion. In addition, a feedback structure is proposed to transfer the fusion results back to the former two modules to improve their stability. Besides, a scale adaptive strategy is presented to improve the tracker’s ability to cope with targets with varying shapes. In the end, the effectiveness of the proposed method is verified by measured radar data. The experimental results demonstrate that the proposed method achieves superior performance compared with other traditional algorithms, which simply focus on the target’s motion information. Moreover, this method is robust under complicated scenarios, such as clutter interference, target shape changing, and low signal-to-noise ratio (SNR).

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22

Ma, Jirong, Qinghua Ma, Shujun Yang, Jianqiang zheng, and Shuaiwei Wang. "Survey of state estimation based on variational bayesian inference." Journal of Physics: Conference Series 2352, no. 1 (2022): 012002. http://dx.doi.org/10.1088/1742-6596/2352/1/012002.

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State estimation problem in multiple target tracking usually faces high-dimensional uncertainty, including target model uncertainty, data association uncertainty, deep coupling and so on. Variational Bayesian inference provides a way to get the approximation for high-dimensional intractable problem. In this paper, we give the survey of state estimation based on variational Bayesian inference.

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23

Papa, Guiseppe, Paolo Braca, Steven Horn, Stefano Marano, Vincenzo Matta, and Peter Willett. "Multisensor adaptive bayesian tracking under time-varying target detection probability." IEEE Transactions on Aerospace and Electronic Systems 52, no. 5 (2016): 2193–209. http://dx.doi.org/10.1109/taes.2016.150522.

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24

Brandes, T. Scott, Nilanjan Dasgupta, and Lawrence Carin. "Variational Bayesian particle filtering for underwater target localization and tracking." Journal of the Acoustical Society of America 125, no. 4 (2009): 2578. http://dx.doi.org/10.1121/1.4783800.

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25

Haug, A. J. "Bayesian estimation for target tracking, Part III: Monte Carlo filters." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 5 (2012): 498–512. http://dx.doi.org/10.1002/wics.1210.

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26

Chen, Yu, Luping Xu, Guangmin Wang, Bo Yan, and Jingrong Sun. "An Improved Smooth Variable Structure Filter for Robust Target Tracking." Remote Sensing 13, no. 22 (2021): 4612. http://dx.doi.org/10.3390/rs13224612.

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As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.

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27

Wang, Jian, Tao Zhang, Xiang Xu, and Yao Li. "A Variational Bayesian Based Strong Tracking Interpolatory Cubature Kalman Filter for Maneuvering Target Tracking." IEEE Access 6 (2018): 52544–60. http://dx.doi.org/10.1109/access.2018.2869020.

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28

Madrigal Díaz, Jorge Francisco, and Jean-Bernard Hayet. "Color and motion-based particle filter target tracking in a network of overlapping cameras with multi-threading and GPGPU." Acta Universitaria 23, no. 1 (2013): 9–16. http://dx.doi.org/10.15174/au.2013.355.

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This paper describes an efficient implementation of multiple-target multiple-view tracking in video-surveillance sequences. It takes advantage of the capabilities of multiple core Central Processing Units (CPUs) and of graphical processing units under the Compute Unifie Device Arquitecture (CUDA) framework. The principle of our algorithm is 1) in each video sequence, to perform tracking on all persons to track by independent particle filters and 2) to fuse the tracking results of all sequences. Particle filters belong to the category of recursive Bayesian filters. They update a Monte-Carlo representation of the posterior distribution over the target position and velocity. For this purpose, they combine a probabilistic motion model, i.e. prior knowledge about how targets move (e.g. constant velocity) and a likelihood model associated to the observations on targets. At this first level of single video sequences, the multi-threading library Threading Buildings Blocks (TBB) has been used to parallelize the processing of the per-target independent particle filters. Afterwards at the higher level, we rely on General Purpose Programming on Graphical Processing Units (generally termed as GPGPU) through CUDA in order to fuse target-tracking data collected on multiple video sequences, by solving the data association problem. Tracking results are presented on various challenging tracking datasets.

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29

Hu, Yumei, Quan Pan, Bao Deng, Zhen Guo, Menghua Li, and Lifeng Chen. "Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks." Entropy 25, no. 8 (2023): 1235. http://dx.doi.org/10.3390/e25081235.

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The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the variational density in the iteration and the degree of nonlinearity of the underlying scenario. In this paper, two methods for computing the variational density, namely, the natural gradient method and the simultaneous perturbation stochastic method, are used to implement a variational Bayesian Kalman filter for maneuvering target tracking using Doppler measurements. The latter are collected from a set of sensors subject to single-hop network constraints. We propose a distributed fusion variational Bayesian Kalman filter for a networked maneuvering target tracking scenario and both of the evidence lower bound and the posterior Cramér–Rao lower bound of the proposed methods are presented. The simulation results are compared with centralized fusion in terms of posterior Cramér–Rao lower bounds, root-mean-squared errors and the 3σ bound.

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30

Xu, Wenjie, Huaguo Zhang, Gaiyou Li, and Wanchun Li. "Vardiational Bayesian Hybrid Multi-Bernoulli and CPHD Filters for Superpositional Sensors." Electronics 12, no. 9 (2023): 2083. http://dx.doi.org/10.3390/electronics12092083.

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This paper addresses the problem of multi-target tracking with superpositional sensors, while the covariance matrices of measurement noise are not known. The proposed method is based on the hybrid multi-Bernoulli cardinalized probability hypothesis density (HMB-CPHD) filter, which has been developed for superpositional sensors-based multi-target tracking with known measurement noises. Specifically, we firstly propose the Gaussian mixture (GM) implementation of the HMB-CPHD filter, and then the covariance matrices of measurement noises are augmented into the target state vector, resulting in the Gaussian and inverse Wishart mixture (GIWM) representation of the augmented state. Then the variational Bayesian (VB) method is exploited to approximate the posterior distribution so that it maintains the same form as the prior distribution. A remarkable feature of the proposed method is that it can jointly perform multi-target tracking and measurement noise covariance estimation. The performance of the proposed algorithm is demonstrated via simulations.

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31

Jiang, Defu, Ming Liu, Yiyue Gao, Yang Gao, Wei Fu, and Yan Han. "Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking." Sensors 18, no. 12 (2018): 4416. http://dx.doi.org/10.3390/s18124416.

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The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gaussian mixture implementations. The results show that the proposed approach can improve the accuracy of target state estimation, as well as the robustness.

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Zhang, Gangsheng, Junwei Xie, Haowei Zhang, Weike Feng, Mingjie Liu, and Cong Qin. "Power Allocation Scheme for Multi-Static Radar to Stably Track Self-Defense Jammers." Remote Sensing 16, no. 15 (2024): 2699. http://dx.doi.org/10.3390/rs16152699.

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Due to suppression jamming by jammers, the signal-to-interference-plus-noise ratio (SINR) during tracking tasks is significantly reduced, thereby decreasing the target detection probability of radar systems. This may result in the interruption of the target track. To address this issue, we propose a multi-static radar power allocation algorithm that enhances the detection and tracking performance of multiple radars in relation to their targets by optimizing power resource allocation. Initially, the echo signal model and measurement model of multi-static radar are formulated, followed by the derivation of the Bayesian Cramér–Rao lower bound (BCRLB). The multi-objective optimization method is utilized to establish the objective function for joint tracking and detection, with dynamic adjustment of the weight coefficient to balance the tracking and detection performance of multiple radars. This ensures the reliability and anti-jamming capability of the multi-static radar system. Simulation results indicate that the proposed algorithm can prevent the interruption of jammer tracking and maintain robust tracking performance.

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33

Huang, Yi Hu, Jin Li Wang, and Xi Mei Jia. "Research of Soccer Robot Target Tracking Algorithm Based on Improved CAMShift." Advanced Materials Research 221 (March 2011): 610–14. http://dx.doi.org/10.4028/www.scientific.net/amr.221.610.

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According to the vision needs of robot soccer and CAMShift tracking inefficient in dynamic background, a new tracking algorithm is brought forward to improve the CAMShift in this paper. A real-time updating background model is build, by traversing the search area for all target pixels to statistic and calculate the color probability distribution of the color target, statistical principles and minimum error rate of Bayesian decision theory are used to achieve a more accurate distinction between the target and the background. By comparing with the CAMShift, the new algorithm provides a better robustness in the soccer robot game and can meet the purposes of fast and accurate tracking.

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34

Li, Zhao, Yu, and Wei. "Underwater Bearing-only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter." Entropy 21, no. 8 (2019): 740. http://dx.doi.org/10.3390/e21080740.

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Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low.

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35

Xue, Xirui, Shucai Huang, Daozhi Wei, and Jiahao Xie. "Multiradar Joint Tracking of Cluster Targets Based on Graph-LSTMs." Journal of Sensors 2022 (November 14, 2022): 1–20. http://dx.doi.org/10.1155/2022/8556477.

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The cluster target brings a serious challenge to the traditional multisensor multitarget tracking algorithm because of its large number of members and the cooperative interaction between members. Using multiradar joint tracking cluster target is an alternative method to solve the problem of cluster target tracking, but it inevitably brings the problem of radar-target assignment and tracking information fusion. Aiming at the problem of radar-target assignment and tracking information fusion, a joint tracking method based on graph-long short-term memory neural nets (Graph-LSTMs) is proposed. Firstly, we use multivariable stochastic differential equations (SDE) to model the cooperative interaction of cluster members and transform the derived state space model of cluster members into the same form as the constant velocity (CV) motion model, and the target state equation of cluster which can be used for Bayesian filtering iteration is established. Secondly, based on the detection relationship between radars and cluster members, we introduce the detection confirmation matrix and propose a radar-target assignment method to achieve multiple measurements of single member and detection coverage of all cluster members. Then, each radar uses δ-GLMB filter to estimate the motion state of the assigned targets. Finally, on the basis of spatial discretization, the labels of multiple estimates of cluster member states are obtained. We use the designed Graph-LSTMs to learn the cooperative relationship between target states to fuse the labels and obtain better tracking effect. The experimental results show that the proposed method effectively simulates the cluster motion and realizes the joint estimation of cluster target motion state by multiradar. Our method makes up for the defect that a single radar cannot stably track adjacent multiple targets and achieves better estimation fusion effect than the expectation-maximization (EM) algorithm and mean method.

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Yoon, Ji. "A New Bayesian Edge-Linking Algorithm Using Single-Target Tracking Techniques." Symmetry 8, no. 12 (2016): 143. http://dx.doi.org/10.3390/sym8120143.

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Ji, Ruiping, Yan Liang, and Linfeng Xu. "Recursive Bayesian inference and learning for target tracking with unknown maneuvers." International Journal of Adaptive Control and Signal Processing 36, no. 4 (2022): 1032–44. http://dx.doi.org/10.1002/acs.3389.

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38

Lu, Kelin, Changyin Sun, and Qian Zhu. "Gaussian process‐based Bayesian non‐linear filtering for online target tracking." IET Radar, Sonar & Navigation 14, no. 3 (2020): 448–58. http://dx.doi.org/10.1049/iet-rsn.2019.0495.

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39

Fung, Robert. "Target identification with Bayesian networks in a multiple hypothesis tracking system." Optical Engineering 36, no. 3 (1997): 684. http://dx.doi.org/10.1117/1.601266.

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Xiang, Yijian, Murat Akcakaya, Satyabrata Sen, Deniz Erdogmus, and Arye Nehorai. "Target tracking via recursive Bayesian state estimation in cognitive radar networks." Signal Processing 155 (February 2019): 157–69. http://dx.doi.org/10.1016/j.sigpro.2018.09.035.

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41

Hosseini, Soheil Sadat, Mohsin M. Jamali, and Simo Särkkä. "Variational Bayesian adaptation of noise covariances in multiple target tracking problems." Measurement 122 (July 2018): 14–19. http://dx.doi.org/10.1016/j.measurement.2018.02.055.

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Tomic, Slavisa, Marko Beko, Rui Dinis, Milan Tuba, and Nebojsa Bacanin. "Bayesian methodology for target tracking using combined RSS and AoA measurements." Physical Communication 25 (December 2017): 158–66. http://dx.doi.org/10.1016/j.phycom.2017.10.005.

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He, Shaoming, Hyo-Sang Shin, and Antonios Tsourdos. "Constrained Multiple Model Bayesian Filtering for Target Tracking in Cluttered Environment." IFAC-PapersOnLine 50, no. 1 (2017): 425–30. http://dx.doi.org/10.1016/j.ifacol.2017.08.192.

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44

Zhang, Wanying, Feng Yang, and Yan Liang. "A Bayesian Framework for Joint Target Tracking, Classification, and Intent Inference." IEEE Access 7 (2019): 66148–56. http://dx.doi.org/10.1109/access.2019.2917541.

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45

Tan, Bin, Zhixiong Ma, Xichan Zhu, et al. "Tracking of Multiple Static and Dynamic Targets for 4D Automotive Millimeter-Wave Radar Point Cloud in Urban Environments." Remote Sensing 15, no. 11 (2023): 2923. http://dx.doi.org/10.3390/rs15112923.

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This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion state estimation and target contour information. The proposed algorithm includes several steps, starting with the estimation of the ego vehicle’s velocity information using the radial velocity information of the millimeter-wave radar point cloud. Different clustering suggestions are then obtained using a density-based clustering method, and correlation regions of the targets are obtained based on these clustering suggestions. The binary Bayesian filtering method is then used to determine whether the targets are dynamic or static targets based on their distribution characteristics. For dynamic targets, Kalman filtering is used to estimate and update the state of the target using trajectory and velocity information, while for static targets, the rolling ball method is used to estimate and update the shape contour boundary of the target. Unassociated measurements are estimated for the contour and initialized for the trajectory, and unassociated trajectory targets are selectively retained and deleted. The effectiveness of the proposed method is verified using real data. Overall, the proposed target tracking algorithm based on 4D millimeter-wave radar point cloud information has the potential to improve the accuracy and reliability of target tracking in autonomous driving applications, providing more comprehensive motion state and target contour information for better decision making.

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Yang, Jin Long, Hong Bing Ji, and Jin Mang Liu. "A Maneuvering Target Tracking Algorithm Based on Gaussian Filter for Multiple Passive Sensors." Key Engineering Materials 467-469 (February 2011): 447–52. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.447.

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When tracking a maneuvering target by multiple passive sensors, two problems need to be considered, one is the nonlinear problem, another is the maneuvering problem. Taking these into account, a Gaussian ﬁlter (GF) for nonlinear Bayesian estimation is introduced based on a deterministic sample selection scheme, which can solve the nonlinear problem better than the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Then, a new maneuvering target tracking algorithm is proposed based on the GF and Interacting Multiple Mode (IMM), called IMM-GF method in this paper. Simulation results show that the proposed method has better performance than the IMM-EKF and IMM-UKF in tracking a maneuvering target for multiple passive sensors.

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Wang, Kuiwu, Qin Zhang, Guimei Zheng, and Xiaolong Hu. "Multi-Target Tracking AA Fusion Method for Asynchronous Multi-Sensor Networks." Sensors 23, no. 21 (2023): 8751. http://dx.doi.org/10.3390/s23218751.

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Aiming at the problem of asynchronous multi-target tracking, this paper studies the AA fusion optimization problem of multi-sensor networks. Firstly, each sensor node runs a PHD filter, and the measurement information obtained from different sensor nodes in the fusion interval is flood communicated into composite measurement information. The Gaussian component representing the same target is associated with a subset by distance correlation. Then, the Bayesian Cramér–Rao Lower Bound of the asynchronous multi-target-tracking error, including radar node selection, is derived by combining the composite measurement information representing the same target. On this basis, a multi-sensor-network-optimization model for asynchronous multi-target tracking is established. That is, to minimize the asynchronous multi-target-tracking error as the optimization objective, the adaptive optimization design of the selection method of the sensor nodes in the sensor network is carried out, and the sequential quadratic programming (SQP) algorithm is used to select the most suitable sensor nodes for the AA fusion of the Gaussian components representing the same target. The simulation results show that compared with the existing algorithms, the proposed algorithm can effectively improve the asynchronous multi-target-tracking accuracy of multi-sensor networks.

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Shi, Yifang, Sundas Qayyum, Sufyan Ali Memon, et al. "A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements." Sensors 20, no. 14 (2020): 3821. http://dx.doi.org/10.3390/s20143821.

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Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD).

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Dogancay, Kutluyil. "Optimal Geometries for AOA Localization in the Bayesian Sense." Sensors 22, no. 24 (2022): 9802. http://dx.doi.org/10.3390/s22249802.

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This paper considers the optimal sensor placement problem for angle-of-arrival (AOA) target localization in the 2D plane with a Gaussian prior. Optimal sensor locations are analytically determined for a single AOA sensor using the D- and A-optimality criteria and an approximation of the Bayesian Fisher information matrix (BFIM). Optimal sensor placement is shown to align with the minor axis of the prior covariance error ellipse for both optimality criteria. The approximate BFIM is argued to be valid for a sufficiently small prior covariance compared with the target range. Optimal sensor placement results obtained for Bayesian target localization are extended to manoeuvring target tracking. For sensor trajectory optimization subject to turn-rate constraints, numerical search methods based on the D- and A-optimality criteria as well as a new closed-form projection algorithm that aims to achieve alignment with the minor axis of the prior error ellipse are proposed. It is observed that the two optimality criteria generate significantly different optimal sensor trajectories despite having the same optimal sensor placement for the localization of a stationary target. Analysis results and the performance of the sensor trajectory optimization methods are demonstrated with simulation examples. It is observed that the new closed-form projection algorithm achieves superior tracking performance compared with the two numerical search methods.

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Liu, Bin, and Chengpeng Hao. "Sequential Bearings-Only-Tracking Initiation with Particle Filtering Method." Scientific World Journal 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/489121.

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The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement (a.k.a the data association problem). In addition, the nonlinear measurements lead to a non-Gaussian posterior probability density function (pdf) in the optimal Bayesian sequential estimation framework. The consequence of this nonlinear/non-Gaussian context is the absence of a closed-form solution. This paper models the linkage uncertainty and the nonlinear/non-Gaussian estimation problem jointly with solid Bayesian formalism. A particle filtering (PF) algorithm is derived for estimating the model’s parameters in a sequential manner. Numerical results show that the proposed solution provides a significant benefit over the most commonly used methods, IPDA and IMMPDA. The posterior Cramér-Rao bounds are also involved for performance evaluation.

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