Relevant bibliographies by topics / Bayesian target tracking

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bayesian target tracking'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Bayesian target tracking"

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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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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
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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 th
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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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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 as
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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
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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, thi
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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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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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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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Dissertations / Theses on the topic "Bayesian target tracking"

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Biresaw, Tewodros Atanaw. "Self-correcting Bayesian target tracking." Thesis, Queen Mary, University of London, 2015. http://qmro.qmul.ac.uk/xmlui/handle/123456789/7925.

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Visual tracking, a building block for many applications, has challenges such as occlusions,illumination changes, background clutter and variable motion dynamics that may degrade the tracking performance and are likely to cause failures. In this thesis, we propose Track-Evaluate-Correct framework (self-correlation) for existing trackers in order to achieve a robust tracking. For a tracker in the framework, we embed an evaluation block to check the status of tracking quality and a correction block to avoid upcoming failures or to recover from failures. We present a generic representation and for
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Gordon, Neil. "Bayesian methods for tracking." Thesis, Imperial College London, 1993. http://hdl.handle.net/10044/1/7783.

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Cevher, Volkan. "A Bayesian Framework for Target Tracking using Acoustic and Image Measurements." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6824.

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Target tracking is a broad subject area extensively studied in many engineering disciplines. In this thesis, target tracking implies the temporal estimation of target features such as the target's direction-of-arrival (DOA), the target's boundary pixels in a sequence of images, and/or the target's position in space. For multiple target tracking, we have introduced a new motion model that incorporates an acceleration component along the heading direction of the target. We have also shown that the target motion parameters can be considered part of a more general feature set for target tracking,
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Papakis, Ioannis. "A Bayesian Framework for Multi-Stage Robot, Map and Target Localization." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93024.

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This thesis presents a generalized Bayesian framework for a mobile robot to localize itself and a target, while building a map of the environment. The proposed technique builds upon the Bayesian Simultaneous Robot Localization and Mapping (SLAM) method, to allow the robot to localize itself and the environment using map features or landmarks in close proximity. The target feature is distinguished from the rest of features since the robot has to navigate to its location and thus needs to be observed from a long distance. The contribution of the proposed approach is on enabling the robot to trac
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Stein, Andrew Neil. "Adaptive image segmentation and tracking : a Bayesian approach." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/13397.

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ur-Rehman, Ata. "Bayesian-based techniques for tracking multiple humans in an enclosed environment." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/14174.

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This thesis deals with the problem of online visual tracking of multiple humans in an enclosed environment. The focus is to develop techniques to deal with the challenges of varying number of targets, inter-target occlusions and interactions when every target gives rise to multiple measurements (pixels) in every video frame. This thesis contains three different contributions to the research in multi-target tracking. Firstly, a multiple target tracking algorithm is proposed which focuses on mitigating the inter-target occlusion problem during complex interactions. This is achieved with the help
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Ozkan, Emre. "Particle Methods For Bayesian Multi-object Tracking And Parameter Estimation." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610986/index.pdf.

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In this thesis a number of improvements have been established for specific methods which utilize sequential Monte Carlo (SMC), aka. Particle filtering (PF) techniques. The first problem is the Bayesian multi-target tracking (MTT) problem for which we propose the use of non-parametric Bayesian models that are based on time varying extension of Dirichlet process (DP) models. The second problem studied in this thesis is an important application area for the proposed DP based MTT method<br>the tracking of vocal tract resonance frequencies of the speech signals. Lastly, we investigate SMC based par
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Daniyan, Abdullahi. "Advanced signal processing techniques for multi-target tracking." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/35277.

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The multi-target tracking problem essentially involves the recursive joint estimation of the state of unknown and time-varying number of targets present in a tracking scene, given a series of observations. This problem becomes more challenging because the sequence of observations is noisy and can become corrupted due to miss-detections and false alarms/clutter. Additionally, the detected observations are indistinguishable from clutter. Furthermore, whether the target(s) of interest are point or extended (in terms of spatial extent) poses even more technical challenges. An approach known as ran
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Brau, Avila Ernesto. "Bayesian Data Association for Temporal Scene Understanding." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/312653.

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Understanding the content of a video sequence is not a particularly difficult problem for humans. We can easily identify objects, such as people, and track their position and pose within the 3D world. A computer system that could understand the world through videos would be extremely beneficial in applications such as surveillance, robotics, biology. Despite significant advances in areas like tracking and, more recently, 3D static scene understanding, such a vision system does not yet exist. In this work, I present progress on this problem, restricted to videos of objects that move in smoothly
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Bryan, Everett A. "Cooperative Target Tracking Enhanced with the Sequence Memoizer." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3814.

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Target tracking is an important part of video surveillance from a UAV. Tracking a target in an urban environment can be difficult because of the number of occlusions present in the environment. If multiple UAVs are used to track a target and the target behavior is learned autonomously by the UAV then the task may become easier. This thesis explores the hypothesis that an existing cooperative control algorithm can be enhanced by a language modeling algorithm to improve over time the target tracking performance of one or more ground targets in a dense urban environment. Observations of target be
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Books on the topic "Bayesian target tracking"

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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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Book chapters on the topic "Bayesian target tracking"

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Stone, Lawrence D., Roy L. Streit, and Stephen L. Anderson. "Bayesian Single Target Tracking." In Studies in Big Data. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32242-6_2.

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Vargas, Juan E., Kiran Tvalarparti, and Zhaojun Wu. "Target Tracking with Bayesian Estimation." In Multiagent Systems, Artificial Societies, and Simulated Organizations. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0363-7_5.

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Vo, Ba-Ngu, Ba-Tuong VO, and Daniel Clark. "Bayesian Multiple Target Filtering Using Random Finite Sets." In Integrated Tracking, Classification, and Sensor Management. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118450550.ch03.

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Manfredotti, Cristina, and Enza Messina. "Relational Dynamic Bayesian Networks to Improve Multi-target Tracking." In Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04697-1_49.

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Achutegui, Katrin, Javier Rodas, Carlos J. Escudero, and Joaquín Míguez. "Bayesian Filtering Methods for Target Tracking in Mixed Indoor/Outdoor Environments." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29479-2_13.

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Snoussi, Hichem, Paul Honeine, and Cédric Richard. "Kernel Variational Approach for Target Tracking in a Wireless Sensor Network." In Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118827253.ch10.

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Elayaraja, Alonshia S. "Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking." In Advances in Business Information Systems and Analytics. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5522-3.ch001.

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Many applications in wireless sensor networks perform localization of nodes over an extended period of time. Optimal selection algorithm poses new challenges to the overall transmission power levels for target detection, and thus, localized energy optimized sensor management strategies are necessary for improving the accuracy of target tracking. In this chapter, a proposal plan to develop a Bayesian localized energy optimized sensor distribution scheme for efficient target tracking in wireless sensor network is designed. The sensor node localization is done with Bayesian average, which estimat
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"A Spherical Constant Velocity Model for Target Tracking in Three Dimensions." In Bayesian Estimation and Tracking. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118287798.ch18.

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Stone, Lawrence. "Bayesian Approach to Multiple-Target Tracking*." In Handbook of Multisensor Data Fusion. CRC Press, 2008. http://dx.doi.org/10.1201/9781420053098.ch12.

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Stone, Lawrence. "A Bayesian Approachto Multiple- Target Tracking*." In Multisensor Data Fusion, 2 Volume Set. CRC Press, 2001. http://dx.doi.org/10.1201/9781420038545.ch10.

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Conference papers on the topic "Bayesian target tracking"

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Yang, Huimin, and Man Chen. "Variational Bayesian-based multiextended target tracking." In 4th International Conference on Green Communication, Network, and Internet of Things (CNIoT 2024), edited by Xiangjie Kong and Cheng Siong Chin. SPIE, 2024. http://dx.doi.org/10.1117/12.3052488.

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Wei, Xinwei, Yiru Lin, Linao Zhang, Zhiyuan Zou, Jianwei Wei, and Wei Yi. "Transformer-based Multi-Target Tracking with Bayesian Perspective." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706292.

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Liu, Xingchi, and Lyudmila Mihaylova. "Active Sensing for Target Tracking: A Bayesian Optimisation Approach." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706282.

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Reed, C. M. "Bayesian track and plot management." In IEE Colloquium. Target Tracking: Algorithms and Applications. IEE, 1999. http://dx.doi.org/10.1049/ic:19990509.

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Tansley, J., D. Lewis, T. Worrall, and P. Thomas. "Bayesian methods for NBC defence." In IEE Colloquium on Target Tracking and Data Fusion. IEE, 1998. http://dx.doi.org/10.1049/ic:19980425.

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Gordon, N. "Bayesian target selection after group pattern distortion." In IEE Colloquium on Target Tracking and Data Fusion. IEE, 1996. http://dx.doi.org/10.1049/ic:19961351.

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Theobald, R. "A Bayesian algorithm to address the radar/ESM track association problem." In Target Tracking 2004: Algorithms and Applications. IEE, 2004. http://dx.doi.org/10.1049/ic:20040058.

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Black, J. V. "A hybrid parametric, non-parametric approach to Bayesian target tracking." In IEE Colloquium on Target Tracking and Data Fusion. IEE, 1996. http://dx.doi.org/10.1049/ic:19961352.

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Kulmon, Pavel. "Bayesian Deghosting Algorithm for Multiple Target Tracking." In 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2020. http://dx.doi.org/10.1109/mfi49285.2020.9235215.

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Kim, Kwang H. "Bayesian inference network: applications to target tracking." In Aerospace Sensing, edited by Oliver E. Drummond. SPIE, 1992. http://dx.doi.org/10.1117/12.139384.

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