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Journal articles on the topic 'Multisensor-multitarget tracking systems'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Li, Song, Yongmei Cheng, Huibin Wang, and Shibo Gao. "Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 4 (2020): 797–805. http://dx.doi.org/10.1051/jnwpu/20203840797.

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In multisensor systems, the signal processing delay, measurement acquisition delay, and other factors will lead to imprecisely time-stamped measurements, namely, the problem of time-offset. To deal with the measurement time offsets in distributed multisensor systems, a distributed multisensor multitarget tracking algorithm with time-offset registration is proposed. The local processors track multiple targets in the presence of false alarms and missed detections based on the joint probabilistic data association (JPDA) algorithm and the extended Kalman filter (EKF), providing the time-biased loc

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2

Zhong, Yuhao, Zhihao Yang, Ting Li, and Yuting Zhang. "An Information-Entropy-Based Hierarchical Serialization Allocation Method for UAV Tracking in 6G Networks." Wireless Communications and Mobile Computing 2022 (September 7, 2022): 1–15. http://dx.doi.org/10.1155/2022/3233789.

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Unmanned aerial vehicles (UAVs) play an important role in future 6G networks, which can be used to assist cellular networks in setting up temporary networks to provide communication services when network access demand is intense. It is critical to design a UAV tracking method with high efficiency and high precision under active sensor radiation control to build a reliable network of UAVs. Multisensor cooperative multitarget tracking for UAVs with high accuracy is an alternative solution to meet the performance requirements of 6G. In this paper, an information-entropy-based multisensor to multi

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3

Pao, Lucy Y. "Multisensor multitarget mixture reduction algorithms for tracking." Journal of Guidance, Control, and Dynamics 17, no. 6 (1994): 1205–11. http://dx.doi.org/10.2514/3.21334.

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4

Aziz, Ashraf M., Murali Tummala, and Roberto Cristi. "Fuzzy logic data correlation approach in multisensor–multitarget tracking systems." Signal Processing 76, no. 2 (1999): 195–209. http://dx.doi.org/10.1016/s0165-1684(99)00008-0.

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5

Balakrishnan, S. N., B. D. Tapley, and B. E. Schutz. "Enhancement of data separability in multisensor-multitarget tracking problems." Journal of Guidance, Control, and Dynamics 12, no. 6 (1989): 938–40. http://dx.doi.org/10.2514/3.20506.

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6

Amelina, Natalia, Victoria Erofeeva, Oleg Granichin, et al. "Consensus-based Distributed Algorithm for Multisensor-Multitarget Tracking under Unknown–but–Bounded Disturbances." IFAC-PapersOnLine 53, no. 2 (2020): 3589–95. http://dx.doi.org/10.1016/j.ifacol.2020.12.1756.

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7

Xie, Jiahao, Shucai Huang, Daozhi Wei, and Zhaoyu Zhang. "Multisensor Dynamic Alliance Control Problem Based on Fuzzy Set Theory in the Mission of Target Detecting and Tracking." Journal of Sensors 2022 (September 22, 2022): 1–13. http://dx.doi.org/10.1155/2022/7919808.

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A multisensor alliance is established by the activation of tasks or occurrences. It is also characterized as a multisensor dynamic alliance since it originates with mission development and disintegrates with task accomplishment. To overcome the constraint that a single sensor can only gather a one-sided, little amount of erroneous target information, each sensor in the dynamic alliance has diverse information collection capabilities and implements a cooperative methodology to complete the target mission. This paper emphasizes on alliance formation in multisensor dynamic alliance control under

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8

Dezert, J., A. Tchamova, and P. Konstantinova. "The Impact of the Quality Assessment of Optimal Assignment for Data Association in a Multitarget Tracking Context." Cybernetics and Information Technologies 15, no. 7 (2015): 88–98. http://dx.doi.org/10.1515/cait-2015-0092.

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Abstract The main purpose of this paper is to apply and to test the performance of a new method, based on belief functions, proposed by Dezert et al. in order to evaluate the quality of the individual association pairings provided in the optimal data association solution for improving the performances of multisensor-multitarget tracking systems. The advantages of its implementation in an illustrative realistic surveillance context, when some of the association decisions are unreliable and doubtful and lead to potentially critical mistake, are discussed. A comparison with the results obtained o

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9

Zheng, Feng, Yu Tian, Weicong Zhan, Jiancheng Yu, and Kaizhou Liu. "A Gaussian mixture multiple-model belief propagation filter for multisensor-multitarget tracking." Signal Processing 220 (July 2024): 109473. http://dx.doi.org/10.1016/j.sigpro.2024.109473.

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10

Xu, Zhixuan, Yu Wei, Xiaobao Qin, and Pengfei Guo. "The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection." Sensors 24, no. 5 (2024): 1508. http://dx.doi.org/10.3390/s24051508.

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Some fusion criteria in multisensor and multitarget motion tracking cannot be directly applied to nonlinear motion models, as the fusion accuracy applied in nonlinear systems is relatively low. In response to the above issue, this study proposes a distributed Gaussian mixture cardinality jumping Markov-cardinalized probability hypothesis density (GM-JMNS-CPHD) filter based on a generalized inverse covariance intersection. The state estimation of the JMNS-CPHD filter combines the state evaluation of traditional CPHD filters with the state estimation of jump Markov systems, estimating the target

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11

Amelin, Konstantin, Oleg Granichin, Anna Sergeenko, and Zeev V. Volkovich. "Emergent Intelligence via Self-Organization in a Group of Robotic Devices." Mathematics 9, no. 12 (2021): 1314. http://dx.doi.org/10.3390/math9121314.

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Networked systems control is a known problem complicated because of the need to work with large groups of elementary agents. In many applications, it is impossible (or difficult) to validate agent movement models and provide sufficiently reliable control actions at the elementary system components level. The evolution of agent subgroups (clusters) leads to additional uncertainty in the studied control systems. We focus on new decentralized control methods based on local communications in complex multiagent dynamical systems. The problem of intelligence in a complex world is considered in conne

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12

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. Fir

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13

Wang, Kuiwu, Qin Zhang, and Xiaolong Hu. "Improved Distributed Multisensor Fusion Method Based on Generalized Covariance Intersection." Journal of Sensors 2022 (October 28, 2022): 1–22. http://dx.doi.org/10.1155/2022/6348938.

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In response to the multitarget tracking problem of distributed sensors with a limited detection range, a distributed sensor measurement complementary Gaussian component correlation GCI fusion tracking method is proposed on the basis of the probabilistic hypothesis density filtering tracking theory. First, the sensor sensing range is extended by complementing the measurements. In this case, the multitarget density product is used to classify whether the measurements belong to the intersection region of the detection range. The local intersection region is complemented only once to reduce the co

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14

"Multitarget-Multisensor Tracking: Principles and Techniques [BOOKSHELF]." IEEE Control Systems 16, no. 1 (1996): 93. http://dx.doi.org/10.1109/mcs.1996.482170.

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15

Lifang, Hu, He You, Guan Xin, Deng Yong, and Han Deqiang. "A New Probabilistic Transformation in Generalized Power Space." July 7, 2011. https://doi.org/10.5281/zenodo.232991.

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The mapping from the belief to the probability domain is a controversial issue, whose original purpose is to make (hard) decision, but for contrariwise to erroneous widespread idea/claim, this is not the only interest for using such mappings nowadays. Actually the probabilistic transformations of belief mass assignments are very useful in modern multitarget multisensor tracking systems where one deals with soft decisions, especially when precise belief structures are not always available due to the existence of uncertainty in human being’s subjective judgments.

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16

Jean, Dezert, and Benameur Kaouthar. "On the Quality of Optimal Assignment for Data Association." July 1, 2015. https://doi.org/10.5281/zenodo.22626.

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In this paper, we present a method based on belief functions to evaluate the quality of the optimal assignment solution of a classical association problem encountered in multiple target tracking applications. The purpose of this work is not to provide a new algorithm for solving the assignment problem, but a solution to estimate the quality of the individual associations (pairings) given in the optimal assignment solution. To the knowledge of authors, this problem has not been addressed so far in the literature and its solution may have practical aspects for 

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17

Chang, Dah-Chung, and Yu-Cheng Chang. "Investigation of Weighted Least Squares Methods for Multitarget Tracking with Multisensor Data Fusion." Journal of Signal Processing Systems, July 1, 2023. http://dx.doi.org/10.1007/s11265-023-01878-4.

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18

Tafti, Abdolreza Dehghani, and Nasser Sadati. "Adaptive Neuro-Fuzzy Inference System in Fuzzy Measurement to Track Association." Journal of Dynamic Systems, Measurement, and Control 132, no. 2 (2010). http://dx.doi.org/10.1115/1.4000663.

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The main issue in a surveillance environment is the target tracking. The most important concern in this problem is the association of the various measurements with the existing target tracks. The fuzzy c-means data association (FCMDA) algorithm, based on the fuzzy c-means (FCM) algorithm, is an efficient solution for the problem of measurement to track association in a multisensor multitarget environment. It has a high accuracy in measurement to track association when targets are far from each other. However, its accuracy remains low when targets are close to one another. The FCMDA algorithm p

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