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Journal articles on the topic 'Distributed tracking'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Mazzaferri, Javier, Stephane Lefrancois, and Santiago Costantino. "Tracking Inhomogeneously Distributed Particles." Biophysical Journal 106, no. 2 (January 2014): 808a. http://dx.doi.org/10.1016/j.bpj.2013.11.4430.

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2

Kırlı, Dilsun. "Distributed call-tracking for security." Computer Languages, Systems & Structures 28, no. 1 (April 2002): 129–54. http://dx.doi.org/10.1016/s0096-0551(02)00010-3.

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3

Ferramosca, A., D. Limon, I. Alvarado, and E. F. Camacho. "Cooperative distributed MPC for tracking." Automatica 49, no. 4 (April 2013): 906–14. http://dx.doi.org/10.1016/j.automatica.2013.01.019.

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4

Taj, Murtaza, and Andrea Cavallaro. "Distributed and Decentralized Multicamera Tracking." IEEE Signal Processing Magazine 28, no. 3 (May 2011): 46–58. http://dx.doi.org/10.1109/msp.2011.940281.

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5

Coraluppi, S., and C. Carthel. "Distributed tracking in multistatic sonar." IEEE Transactions on Aerospace and Electronic Systems 41, no. 3 (July 2005): 1138–47. http://dx.doi.org/10.1109/taes.2005.1541460.

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6

Ferramosca, A., D. Limon, J. B. Rawlings, and E. F. Camacho. "Cooperative distributed MPC for tracking." IFAC Proceedings Volumes 44, no. 1 (January 2011): 1584–89. http://dx.doi.org/10.3182/20110828-6-it-1002.03581.

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7

Gao, Lin, Giorgio Battistelli, and Luigi Chisci. "Event-Triggered Distributed Multitarget Tracking." IEEE Transactions on Signal and Information Processing over Networks 5, no. 3 (September 2019): 570–84. http://dx.doi.org/10.1109/tsipn.2019.2924196.

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8

Raju, Lakshmi K., Febi Ibrahim, and P. Muralikrishna. "Distributed Target Localization and Tracking Using Distributed Bearing Sensors." Procedia Computer Science 93 (2016): 728–34. http://dx.doi.org/10.1016/j.procs.2016.07.280.

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9

Lee, Hyeon-Seok, and Jae-Jung Yun. "Advanced MPPT Algorithm for Distributed Photovoltaic Systems." Energies 12, no. 18 (September 19, 2019): 3576. http://dx.doi.org/10.3390/en12183576.

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The basic and adaptive maximum power point tracking algorithms have been studied for distributed photovoltaic systems to maximize the energy production of a photovoltaic (PV) module. However, the basic maximum power point tracking algorithms using a fixed step size, such as perturb and observe and incremental conductance, suffer from a trade-off between tracking accuracy and tracking speed. Although the adaptive maximum power point tracking algorithms using a variable step size improve the maximum power point tracking efficiency and dynamic response of the basic algorithms, these algorithms still have the oscillations at the maximum power point, because the variable step size is sensitive to external factors. Therefore, this paper proposes an enhanced maximum power point tracking algorithm that can have fast dynamic response, low oscillations, and high maximum power point tracking efficiency. To achieve these advantages, the proposed maximum power point tracking algorithm uses two methods that can apply the optimal step size to each operating range. In the operating range near the maximum power point, a small fixed step size is used to minimize the oscillations at the maximum power point. In contrast, in the operating range far from the maximum power point, a variable step size proportional to the slope of the power-voltage curve of PV module is used to achieve fast tracking speed under dynamic weather conditions. As a result, the proposed algorithm can achieve higher maximum power point tracking efficiency, faster dynamic response, and lower oscillations than the basic and adaptive algorithms. The theoretical analysis and performance of the proposed algorithm were verified by experimental results. In addition, the comparative experimental results of the proposed algorithm with the other maximum power point tracking algorithms show the superiority of the proposed algorithm.

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10

Song, Taek Lyul, Hyoung Won Kim, and Darko Musicki. "Distributed (nonlinear) target tracking in clutter." IEEE Transactions on Aerospace and Electronic Systems 51, no. 1 (January 2015): 654–68. http://dx.doi.org/10.1109/taes.2014.130151.

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11

Oh, Songhwai, Inseok Hwang, and Shankar Sastry. "Distributed Multitarget Tracking and Identity Management." Journal of Guidance, Control, and Dynamics 31, no. 1 (January 2008): 12–29. http://dx.doi.org/10.2514/1.26237.

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12

Evers, Christine, Emanuel A. P. Habets, Sharon Gannot, and Patrick A. Naylor. "DoA Reliability for Distributed Acoustic Tracking." IEEE Signal Processing Letters 25, no. 9 (September 2018): 1320–24. http://dx.doi.org/10.1109/lsp.2018.2849579.

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13

Liu, J., M. Chu, and J. E. Reich. "Multitarget Tracking in Distributed Sensor Networks." IEEE Signal Processing Magazine 24, no. 3 (May 2007): 36–46. http://dx.doi.org/10.1109/msp.2007.361600.

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14

Liu, Hui, Fei Chen, Linying Xiang, and Weiyao Lan. "Distributed average tracking with input saturation." Nonlinear Dynamics 90, no. 4 (October 10, 2017): 2827–39. http://dx.doi.org/10.1007/s11071-017-3844-z.

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15

Kögel, Markus, and Rolf Findeisen. "Set-point tracking using distributed MPC." IFAC Proceedings Volumes 46, no. 32 (December 2013): 57–62. http://dx.doi.org/10.3182/20131218-3-in-2045.00097.

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16

Wu, Yi-Chang, Ching-Han Chen, Yao-Te Chiu, and Pi-Wei Chen. "Cooperative People Tracking by Distributed Cameras Network." Electronics 10, no. 15 (July 25, 2021): 1780. http://dx.doi.org/10.3390/electronics10151780.

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In the application of video surveillance, reliable people detection and tracking are always challenging tasks. The conventional single-camera surveillance system may encounter difficulties such as narrow-angle of view and dead space. In this paper, we proposed multi-cameras network architecture with an inter-camera hand-off protocol for cooperative people tracking. We use the YOLO model to detect multiple people in the video scene and incorporate the particle swarm optimization algorithm to track the person movement. When a person leaves the area covered by a camera and enters an area covered by another camera, these cameras can exchange relevant information for uninterrupted tracking. The motion smoothness (MS) metrics is proposed for evaluating the tracking quality of multi-camera networking system. We used a three-camera system for two persons tracking in overlapping scene for experimental evaluation. Most tracking person offsets at different frames were lower than 30 pixels. Only 0.15% of the frames showed abrupt increases in offsets pixel. The experiment results reveal that our multi-camera system achieves robust, smooth tracking performance.

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17

Ma, Liang, Kai Xue, and Ping Wang. "Distributed Multiagent Control Approach for Multitarget Tracking." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/903682.

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In multiagent systems, tracking multiple targets is challenging for two reasons: firstly, it is nontrivial to dynamically deploy networked agents of different types for utility optimization; secondly, information fusion for multitarget tracking is difficult in the presence of uncertainties, such as data association, noise, and clutter. In this paper, we present a novel control approach in distributed manner for multitarget tracking. The control problem is modelled as a partially observed Markov decision process, which is a NP-hard combinatorial optimization problem, by seeking all possible combinations of control commands. To solve this problem efficiently, we assume that the measurement of each agent is independent of other agents’ behavior and provide a suboptimal multiagent control solution by maximizing the local Rényi divergence. In addition, we also provide the SMC implementation of the sequential multi-Bernoulli filter so that each agent can utilize the measurements from neighbouring agents to perform information fusion for accurate multitarget tracking. Numerical studies validate the effectiveness and efficiency of our multiagent control approach for multitarget tracking.

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18

JARD, CLAUDE, and GUY-VINCENT JOURDAN. "INCREMENTAL TRANSITIVE DEPENDENCY TRACKING IN DISTRIBUTED COMPUTATIONS." Parallel Processing Letters 06, no. 03 (September 1996): 427–35. http://dx.doi.org/10.1142/s0129626496000406.

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The notion of causal dependency between events in distributed systems plays a central role in reasoning about distributed program behaviours [14]. Different techniques have been designed to track these dependencies during execution. We suggest a new incremental transitive dependency tracking technique. Once the transitive dependencies are recorded for an observable event, the dependency tracking cost can be reduced by propagating only future dependencies beyond that event. Furthermore, in contrast with the direct dependency tracking technique already proposed in the literature, our technique allows to compute the dependencies among an arbitrary subset of observable events. This gives an interesting filtering capability.

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19

He, Changran, and Jie Huang. "Distributed Formation Tracking for Multiple Quadrotor Helicopters." IFAC-PapersOnLine 53, no. 2 (2020): 14793–98. http://dx.doi.org/10.1016/j.ifacol.2020.12.1911.

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20

ALTINER, İHSAN BERK, MUSTAFA DOĞAN, and JANSET DAŞDEMİR. "Adaptive output tracking of distributed parameter systems." Turkish Journal of Electrical Engineering and Computer Sciences 30, no. 3 (January 1, 2022): 518–30. http://dx.doi.org/10.55730/1300-0632.3795.

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21

Alimadadi, Mohammadreza, Milica Stojanovic, and Pau Closas. "Delay-Tolerant Distributed Inference in Tracking Networks." Sensors 21, no. 17 (August 26, 2021): 5747. http://dx.doi.org/10.3390/s21175747.

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This paper discusses asynchronous distributed inference in object tracking. Unlike many studies, which assume that the delay in communication between partial estimators and the central station is negligible, our study focuses on the problem of asynchronous distributed inference in the presence of delays. We introduce an efficient data fusion method for combining the distributed estimates, where delay in communications is not negligible. To overcome the delay, predictions are made for the state of the system based on the most current available information from partial estimators. Simulation results show the efficacy of the methods proposed.

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22

Cheng, Long, Shuang-Hua Yang, and Shuai Li. "Deployment and Tracking in Distributed Sensor Networks." International Journal of Distributed Sensor Networks 10, no. 6 (January 2014): 657971. http://dx.doi.org/10.1155/2014/657971.

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23

Zhang, Y., H. Leung, T. Lo, and J. Litva. "Distributed sequential nearest neighbour multitarget tracking algorithm." IEE Proceedings - Radar, Sonar and Navigation 143, no. 4 (1996): 255. http://dx.doi.org/10.1049/ip-rsn:19960317.

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24

Brooks, R. R., C. Griffin, and D. S. Friedlander. "Self-Organized Distributed Sensor Network Entity Tracking." International Journal of High Performance Computing Applications 16, no. 3 (August 2002): 207–19. http://dx.doi.org/10.1177/10943420020160030201.

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25

Yang, Qingkai, Ming Cao, Hector Garcia de Marina, Hao Fang, and Jie Chen. "Distributed formation tracking using local coordinate systems." Systems & Control Letters 111 (January 2018): 70–78. http://dx.doi.org/10.1016/j.sysconle.2017.11.004.

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26

Tong, Yongxin, Xiaofei Zhang, and Lei Chen. "Tracking frequent items over distributed probabilistic data." World Wide Web 19, no. 4 (May 5, 2015): 579–604. http://dx.doi.org/10.1007/s11280-015-0341-5.

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27

Battistelli, Giorgio, Luigi Chisci, Claudio Fantacci, Alfonso Farina, and Antonio Graziano. "Consensus CPHD Filter for Distributed Multitarget Tracking." IEEE Journal of Selected Topics in Signal Processing 7, no. 3 (June 2013): 508–20. http://dx.doi.org/10.1109/jstsp.2013.2250911.

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28

Falsone, Alessandro, Ivano Notarnicola, Giuseppe Notarstefano, and Maria Prandini. "Tracking-ADMM for distributed constraint-coupled optimization." Automatica 117 (July 2020): 108962. http://dx.doi.org/10.1016/j.automatica.2020.108962.

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29

Hao, Qi, David J. Brady, Bob D. Guenther, John B. Burchett, Mohan Shankar, and Steve Feller. "Human Tracking With Wireless Distributed Pyroelectric Sensors." IEEE Sensors Journal 6, no. 6 (December 2006): 1683–96. http://dx.doi.org/10.1109/jsen.2006.884562.

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30

Dall'Anese, Emiliano, Seung-Jun Kim, and Georgios B. Giannakis. "Channel Gain Map Tracking via Distributed Kriging." IEEE Transactions on Vehicular Technology 60, no. 3 (March 2011): 1205–11. http://dx.doi.org/10.1109/tvt.2011.2113195.

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31

Dong, Wenjie. "Distributed tracking control of networked chained systems." International Journal of Control 86, no. 12 (December 2013): 2159–74. http://dx.doi.org/10.1080/00207179.2013.803156.

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32

Chen, Gang, and Frank L. Lewis. "Distributed Tracking Control for Networked Mechanical Systems." Asian Journal of Control 14, no. 6 (May 31, 2012): 1459–69. http://dx.doi.org/10.1002/asjc.537.

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33

Carnevale, Guido, Ivano Notarnicola, Lorenzo Marconi, and Giuseppe Notarstefano. "Triggered Gradient Tracking for asynchronous distributed optimization." Automatica 147 (January 2023): 110726. http://dx.doi.org/10.1016/j.automatica.2022.110726.

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34

Hu, PG, and XQ Chen. "Research on distributed network attack source tracking method based on interference suppression." Journal of Physics: Conference Series 2158, no. 1 (January 1, 2022): 012031. http://dx.doi.org/10.1088/1742-6596/2158/1/012031.

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Abstract In order to improve the security of distributed network operation, a distributed network attack source tracking method based on interference suppression is proposed. According to the structural characteristics of distributed network, the signal expression relationship between different distributed hosts is constructed, and the generalized cross-correlation weighting function is constructed to suppress the interference of distributed network. Based on this, particle swarm optimization algorithm is used to collect a sufficient number of attack marking information packets through multiple iterations. The interference signal of the separated signal is obtained through blind source separation. After signal separation, the tracking results of different attack source signals are obtained. The experimental results show that compared with the traditional attack source tracking method, this method not only reduces the tracking time, but also greatly improves the tracking accuracy.

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35

Zhaoyong Wang, Zhaoyong Wang, Zhengqing Pan Zhengqing Pan, Qing Ye Qing Ye, Bin Lu Bin Lu, Zujie Fang Zujie Fang, Haiwen Cai Haiwen Cai, and Ronghui Qu Ronghui Qu. "Novel distributed passive vehicle tracking technology using phase sensitive optical time domain reflectometer." Chinese Optics Letters 13, no. 10 (2015): 100603–6. http://dx.doi.org/10.3788/col201513.100603.

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36

Zhang, Qiaoling, Zhe Chen, and Fuliang Yin. "Distributed Marginalized Auxiliary Particle Filter for Speaker Tracking in Distributed Microphone Networks." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, no. 11 (November 2016): 1921–34. http://dx.doi.org/10.1109/taslp.2016.2590146.

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37

Ni, Peng, Bo Zhang, Yafei Song, and Mingliang Zhang. "Multisensor Distributed Dynamic Programming Method for Collaborative Warning and Tracking." Mathematical Problems in Engineering 2020 (May 15, 2020): 1–19. http://dx.doi.org/10.1155/2020/2818416.

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Multisensor distributed dynamic programming for collaborative warning and tracking during antimissile combat serves to meet the tracking accuracy requirements of all ballistic targets in the battlefield under the circumstance of a limited total amount of sensor resources. This paper proposes a method of multisensor distributed dynamic programming for collaborative warning and tracking based on game theory. First, starting from the target tracking algorithm, according to the characteristics of antimissile multisensor combat planning, the box particle filter (BPF) theory capable of distributed filtering and inaccurate measurement is introduced. Using the flight phase characteristics of ballistic targets, a variable structure adaptive multimodel box-based particle filter tracking method is constructed. A box particle filter with the variable structure adaptive interacting multiple model (VSAIMM-BPF) is proposed. The method solves the continuous real-time tracking problem of the ballistic target in all the phases and achieves high tracking accuracy while reducing computational complexity. Then, the motion state of each ballistic target in combat is recursively evaluated by the filtering algorithm, and the calculated sensor information gain is used as a measure to obtain more or better sensor resources for the community of interest to track the corresponding ballistic target through the game. Ultimately, the method achieves distributed dynamic programming.

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38

Cai, Jia, Chang Qiang Huang, and Hai Feng Guo. "Multi-Sensor Cooperative Tracking Using Distributed Nash Q-Learning." Advanced Materials Research 591-593 (November 2012): 1475–78. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1475.

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Traditional target tracking algorithm has a disadvantage of excessive dependence on the environment model. Thus a multi-sensor cooperative tracking method using distributed Nash Q-learning was proposed. Distributed Nash Q-learning with model-free was firstly described. Then sensor action and reward function were defined, which both are very crucial to the learning. Sensor action was only subjected to angle control, and reward function was given by calculating the trace of one time-step prediction error covariance. Nash tragedy can not be directly calculated, therefore, a probability statistics method using Bayesian inference was used to update the Q function. Simulation of passive tracking merely with angle measurements shows that this algorithm can enhance the adaptation to environment change and the tracking accuracy.

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39

Zhang, Ya Peng, Qing Rong Liu, and Ying Jun Ruan. "Optimal Option Analysis of Distributed Generation Equipments." Advanced Materials Research 614-615 (December 2012): 1796–99. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1796.

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In the application of distributed generation technologies, different building have its own heat-to-power ratio, therefore equipment selection is a key point to determine the energy saving ratio. This study analyzes some devices of four different types, from two basic managements, heat-tracking and electricity-tracking. Result shows the optimal heat-to-power ratio of some certain devices and the changing trends were also given out. The energy saving ratio were also calculated.

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40

Zhang, Sen, Wendong Xiao, and Jun Gong. "IMM Filter Based Human Tracking Using a Distributed Wireless Sensor Network." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/895971.

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This paper proposes a human tracking approach in a distributed wireless sensor network. Most of the efforts on human tracking focus on vision techniques. However, most vision-based approaches to moving object detection involve intensive real-time computations. In this paper, we present an algorithm for human tracking using low-cost range wireless sensor nodes which can contribute lower computational burden based on a distributed computing system, while the centralized computing system often makes some information from sensors delay. Because the human target often moves with high maneuvering, the proposed algorithm applies the interacting multiple model (IMM) filter techniques and a novel sensor node selection scheme developed considering both the tracking accuracy and the energy cost which is based on the tacking results of IMM filter at each time step. This paper also proposed a novel sensor management scheme which can manage the sensor node effectively during the sensor node selection and the tracking process. Simulations results show that the proposed approach can achieve superior tracking accuracy compared to the most recent human motion tracking scheme.

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41

Fareh, Raouf, Mohamad Saad, and Maarouf Saad. "Distributed control strategy for flexible link manipulators." Robotica 33, no. 4 (March 13, 2014): 768–86. http://dx.doi.org/10.1017/s0263574714000459.

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SUMMARYThis paper presents a nonlinear distributed control strategy for flexible-link manipulators to solve the tracking control problem in the joint space and cancel vibrations of the links. First, the dynamic of an n-flexible-link manipulator is decomposed into n subsystems. Each subsystem has a pair of one joint and one link. The distributed control strategy is applied to each subsystem starting from the last subsystem. The strategy of control consists in controlling the nth joint and stabilizing the nth link by assuming that the remaining subsystems are stable. Then, going backward to the (n − 1)th subsystem, the same control strategy is applied to each corresponding joint-link subsystem until the first. Sliding mode technique is used to develop the control law of each subsystem and the global stability of the resulting tracking errors is proved using the Lyapunov technique. This algorithm was tested on a two-flexible-link manipulator and gave effective results, a good tracking performance, and capability to eliminate the links' vibrations.

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42

ZHENG Bin-qi, 郑斌琪, 李宝清 LI Bao-qing, 刘华巍 LIU Hua-wei, and 袁晓兵 YUAN Xiao-bing. "Distributed target tracking based on adaptive consensus UKF." Optics and Precision Engineering 27, no. 1 (2019): 260–70. http://dx.doi.org/10.3788/ope.20192701.0260.

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43

Ma, Xiaoyu, Peng Yi, and Jie Chen. "Distributed Gradient Tracking Methods with Finite Data Rates." Journal of Systems Science and Complexity 34, no. 5 (October 2021): 1927–52. http://dx.doi.org/10.1007/s11424-021-1231-9.

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44

Falsone, Alessandro, and Maria Prandini. "Distributed decision-coupled constrained optimization via Proximal-Tracking." Automatica 135 (January 2022): 109938. http://dx.doi.org/10.1016/j.automatica.2021.109938.

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45

Jenabzadeh, Ahmadreza, Behrouz Safarinejadian, Yu Lu, and Weidong Zhang. "Distributed event-triggered target tracking under cyber attacks." Journal of the Franklin Institute 359, no. 5 (March 2022): 2377–402. http://dx.doi.org/10.1016/j.jfranklin.2021.12.020.

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46

Ding, Tie, Shanying Zhu, Cailian Chen, Jinming Xu, and Xinping Guan. "Differentially Private Distributed Resource Allocation via Deviation Tracking." IEEE Transactions on Signal and Information Processing over Networks 7 (2021): 222–35. http://dx.doi.org/10.1109/tsipn.2021.3062985.

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47

Liggins, M. E., Chee-Yee Chong, I. Kadar, M. G. Alford, V. Vannicola, and S. Thomopoulos. "Distributed Fusion Architectures and Algorithms for Target Tracking." Proceedings of the IEEE 85, no. 1 (January 1997): 95–107. http://dx.doi.org/10.1109/jproc.1997.554211.

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48

Brooks, R. R., P. Ramanathan, and A. M. Sayeed. "Distributed target classification and tracking in sensor networks." Proceedings of the IEEE 91, no. 8 (August 2003): 1163–71. http://dx.doi.org/10.1109/jproc.2003.814923.

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49

Liu, Jinqi, and Ge Guo. "Distributed Asynchronous Extended Target Tracking Using Random Matrix." IEEE Sensors Journal 20, no. 2 (January 15, 2020): 947–56. http://dx.doi.org/10.1109/jsen.2019.2944280.

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50

Gruenwedel, Sebastian, Vedran Jelaca, Jorge Oswaldo Nino-Castaneda, Peter van Hese, Dimitri van Cauwelaert, Dirk van Haerenborgh, Peter Veelaert, and Wilfried Philips. "Low-complexity scalable distributed multicamera tracking of humans." ACM Transactions on Sensor Networks 10, no. 2 (January 2014): 1–32. http://dx.doi.org/10.1145/2530282.

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