A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
T.-Y. Chen, X.-M. Hu, Q. Lin, and W.-N. Chen, “Multi-agent swarm optimization with contribution-based cooperation for distributed multi-target localization and data association,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125150
Citation: T.-Y. Chen, X.-M. Hu, Q. Lin, and W.-N. Chen, “Multi-agent swarm optimization with contribution-based cooperation for distributed multi-target localization and data association,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125150
shu

Multi-Agent Swarm Optimization With Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association

doi: 10.1109/JAS.2025.125150
Funds:  This work was supported in part by National Natural Science Foundation of China (62376097), and Guangdong Regional Joint Foundation Key (2022B1515120076)
More Information
    Abstract
  • With the development of communication and computation capabilities on terminal hardware, it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration ability of sensors. In this work, we study distributed optimization for multi-target localization with measurement-to-measurement association (DM2M), where each sensor only accesses its own measurement data without the association of measurements from other sensors. We first reformulate DM2M into a distributed bilevel optimization problem to reduce the search space of negotiated variables caused by the data association among sensors. Then, we propose a multi-agent swarm optimization method with contribution-based cooperation (MASTER). In MASTER, each sensor maintains a particle swarm to represent candidate solutions of target positions. Sensors evolve their particle swarms through two phases of local optimization and neighbor cooperation to locate the target cooperatively. To address the bilevel local objective function, we combine the Kuhn-Munkres algorithm and the competitive swarm optimization for local optimization. To promote sensors to optimize the global objective, we design a contribution-based cooperation method to guide sensors to learn from their neighbors. Through localization experiments for different target numbers and localization dimensions, the proposed algorithm achieves smaller localization errors and more stable consensus than existing algorithms.

     

    • FullText(HTML)
  • loading
    • References(54)
  • [1]
    G. Mao, B. Fidan, and B. D. Anderson, “Wireless sensor network localization techniques,” Computer Networks, vol. 51, no. 10, pp. 2529–2553, 2007. doi: 10.1016/j.comnet.2006.11.018
    [2]
    S. Wang, L. Chen, D. Gu, and H. Hu, “Cooperative localization of AUVs using moving horizon estimation,” IEEE/CAA Journal of Automatica Sinica, vol. 1, no. 1, pp. 68–76, 2014. doi: 10.1109/JAS.2014.7004622
    [3]
    R. A. Khalil, N. Saeed, M. I. Babar, T. Jan, and S. Din, “Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 3, pp. 496–509, 2022. doi: 10.1109/JAS.2021.1004356
    [4]
    H. Zhao, J. Yan, X. Luo, and X. Gua, “Privacy preserving solution for the asynchronous localization of underwater sensor networks,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 6, pp. 1511–1527, 2020. doi: 10.1109/JAS.2020.1003312
    [5]
    W. Huang, Z. Pan, and Z. Xu, “Underwater Cable Localization Method Based on Beetle Swarm Optimization Algorithm,” IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 9, pp. 1893–1895, 2023. doi: 10.1109/JAS.2022.106073
    [6]
    G. Tabella, D. Ciuonzo, N. Paltrinieri, and P. S. Rossi, “Bayesian Fault Detection and Localization Through Wireless Sensor Networks in Industrial Plants,” IEEE Internet of Things Journal, vol. 11, no. 8, pp. 13 231–13 246, 2024. doi: 10.1109/JIOT.2024.3359646
    [7]
    I. Ahmed, S. Din, G. Jeon, F. Piccialli, and G. Fortino, “Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 7, pp. 1253–1270, 2021. doi: 10.1109/JAS.2020.1003453
    [8]
    D. Touska, K. Gkountakos, T. Tsikrika, K. Ioannidis, S. Vrochidis, and I. Kompatsiaris, “Graph-Based Data Association in Multiple Object Tracking: A Survey,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13834 LNCS, pp. 386–398, 2023.
    [9]
    J. H. White and R. W. Beard, “An iterative pose estimation algorithm based on epipolar geometry with application to multi-target tracking,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 4, pp. 942–953, 2020. doi: 10.1109/JAS.2020.1003222
    [10]
    H. Liu, Y. Chen, Y. Huang, X. Cheng, and Q. Xiao, “Study on the localization method of multi-aperture acoustic array based on tdoa,” IEEE Sensors Journal, vol. 21, no. 12, pp. 13 805–13 814, 2021. doi: 10.1109/JSEN.2021.3070811
    [11]
    L. Rakai, H. Song, S. Sun, W. Zhang, and Y. Yang, “Data association in multiple object tracking: A survey of recent techniques,” Expert Systems with Applications, vol. 192, p. 116300.
    [12]
    P. Parmar and M. Zaveri, “Multiple target tracking and data association in wireless sensor network,” in 2012 Fourth Int. Conf. on Computational Intelligence and Communication Networks, Nov. 2012, pp. 158–163.
    [13]
    H. Chen, T. Kirubarajan, and Y. Bar-Shalom, “Tracking of spawning targets with multiple finite resolution sensors,” in Proc. the Fifth Int. Conf. on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997), vol. 2, Jul. 2002, pp. 1511–1518 vol.2.
    [14]
    A. T. Kamal, J. H. Bappy, J. A. Farrell, and A. K. Roy-Chowdhury, “Distributed multi-target tracking and data association in vision networks,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 38, no. 7, pp. 1397–1410, Jul. 2016. doi: 10.1109/TPAMI.2015.2484339
    [15]
    N. F. Sandell and R. Olfati-Saber, “Distributed data association for multi-target tracking in sensor networks,” in 2008 47th IEEE Conf. on Decision and Control. Cancun, Mexico: IEEE, 2008, pp. 1085–1090.
    [16]
    Y. Zhu, A. Vikram, H. Fu, and Y. Guan, “On non-cooperative multiple-target tracking with wireless sensor networks,” IEEE Trans. Wireless Communications, vol. 13, no. 11, pp. 6496–6510, 2014. doi: 10.1109/TWC.2014.2329306
    [17]
    B. Camajori~Tedeschini, M. Brambilla, L. Barbieri, G. Balducci, and M. Nicoli, “Cooperative lidar sensing for pedestrian detection: Data association based on message passing neural networks,” IEEE Trans. Signal Processing, vol. 71, pp. 3028–3042, 2023. doi: 10.1109/TSP.2023.3304002
    [18]
    S. A. R. Kazemi, R. Amiri, and F. Behnia, “Data association for multi-target elliptic localization in distributed mimo radars,” IEEE Communications Letters, vol. 25, no. 9, pp. 2904–2907, Sep. 2021. doi: 10.1109/LCOMM.2021.3087009
    [19]
    T. Ge, R. Tharmarasa, B. Lebel, M. Florea, and T. T. Kirubarajan, “A multidimensional TDOA association algorithm for joint multitarget localization and multisensor synchronization,” IEEE Trans. Aerospace and Electronic Systems, vol. 56, no. 3, pp. 2083–2100, 2020. doi: 10.1109/TAES.2019.2943786
    [20]
    Z. Wei, Z. Duan, and M. Mallick, “Measurement-to-Measurement Association for MDA with A Practical Coarse Gating Strategy,” 2023 26th Int. Conf. on Information Fusion (FUSION), pp. 1–8, 2023.
    [21]
    X. Dang, Q. Cheng, and H. Zhu, “Indoor Multiple Sound Source Localization via Multi-Dimensional Assignment Data Association,” IEEE/ACM Trans. Audio Speech and Language Processing, vol. 27, no. 12, pp. 1944–1956, 2019. doi: 10.1109/TASLP.2019.2935837
    [22]
    A. Ez-Zaidi and S. Rakrak, “A comparative study of target tracking approaches in wireless sensor networks,” Journal of Sensors, vol. 2016, pp. 1–11, 2016.
    [23]
    Y. Zhang, Y. Lou, Y. Hong, and L. Xie, “Distributed projection-based algorithms for source localization in wireless sensor networks,” IEEE Trans. Wireless Communications, vol. 14, no. 6, pp. 3131–3142, 2015. doi: 10.1109/TWC.2015.2402672
    [24]
    Y. Lv, H. Zhang, Z. Wang, and H. Yan, “Distributed localization for dynamic multiagent systems with randomly varying trajectory lengths,” IEEE Trans. Industrial Electronics, vol. 69, no. 9, pp. 9298–9308, Sep. 2022. doi: 10.1109/TIE.2021.3111571
    [25]
    Y. Wang, X. Chen, L. Shi, Y. Cheng, and H. Wang, “A Trust Assessment-Based Distributed Localization Algorithm for Sensor Networks Under Deception Attacks,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 10, pp. 1879–1882, 2022. doi: 10.1109/JAS.2022.105881
    [26]
    S. Wang, X. Jiang, and H. Wymeersch, “Cooperative localization in wireless sensor networks with aoa measurements,” IEEE Trans. Wireless Communications, vol. 21, no. 8, pp. 6760–6773, 2022. doi: 10.1109/TWC.2022.3152426
    [27]
    Y. Zhao, Z. Li, B. Hao, and J. Shi, “Sensor selection for tdoa-based localization in wireless sensor networks with non-line-of-sight condition,” IEEE Trans. Vehicular Technology, vol. 68, no. 10, pp. 9935–9950, 2019. doi: 10.1109/TVT.2019.2936110
    [28]
    D. Dasgupta, G. Hernandez, D. Garrett, P. K. Vejandla, A. Kaushal, R. Yerneni, and J. Simien, “A comparison of multiobjective evolutionary algorithms with informed initialization and kuhn-munkres algorithm for the sailor assignment problem,” in Proc. the 10th Annual Conf. Companion on Genetic and Evolutionary Computation, ser. GECCO '08. New York, NY, USA: Association for Computing Machinery, 2008, p. 2129–2134.
    [29]
    R. Cheng and Y. Jin, “A competitive swarm optimizer for large scale optimization,” IEEE Trans. Cybernetics, vol. 45, no. 2, pp. 191–204, 2015. doi: 10.1109/TCYB.2014.2322602
    [30]
    G. Wen, H. Song, F. Gao, Y. Liang, and L. Zhu, “Target Localization in Asynchronous Distributed MIMO Radar Systems with a Cooperative Target,” IEEE Trans. Wireless Communications, vol. 21, no. 6, pp. 4098–4113, 2022. doi: 10.1109/TWC.2021.3126745
    [31]
    J. A. Costa, N. Patwari, and A. O. Hero, “Distributed weighted-multidimensional scaling for node localization in sensor networks,” ACM Trans. Sensor Networks, vol. 2, no. 1, pp. 39–64, 2006. doi: 10.1145/1138127.1138129
    [32]
    K. W. K. Lui, W.-K. Ma, H. C. So, and F. K. W. Chan, “Semi-definite programming algorithms for sensor network node localization with uncertainties in anchor positions and/or propagation speed,” IEEE Trans. Signal Processing, vol. 57, no. 2, pp. 752–763, 2009. doi: 10.1109/TSP.2008.2007916
    [33]
    J. Nie, “Sum of squares method for sensor network localization,” Computational Optimization and Applications, vol. 43, no. 2, pp. 151–179, 2009. doi: 10.1007/s10589-007-9131-z
    [34]
    S. Srirangarajan, A. H. Tewfik, and Z.-Q. Luo, “Distributed sensor network localization using socp relaxation,” IEEE Trans. Wireless Communications, vol. 7, no. 12, pp. 4886–4895, 2008. doi: 10.1109/T-WC.2008.070241
    [35]
    A. Nedic and A. Ozdaglar, “Distributed subgradient methods for multi-agent optimization,” IEEE Trans. Automatic Control, vol. 54, no. 1, pp. 48–61, Jan. 2009. doi: 10.1109/TAC.2008.2009515
    [36]
    D. Yuan and D. W. C. Ho, “Randomized gradient-free method for multiagent optimization over time-varying networks,” IEEE Trans. Neural Networks and Learning Systems, vol. 26, no. 6, pp. 1342–1347, Jun. 2015. doi: 10.1109/TNNLS.2014.2336806
    [37]
    M. Chamanbaz, G. Notarstefano, F. Sasso, and R. Bouffanais, “Randomized constraints consensus for distributed robust mixed-integer programming,” IEEE Trans. Control of Network Systems, vol. 8, no. 1, pp. 295–306, Mar. 2021. doi: 10.1109/TCNS.2020.3024483
    [38]
    Y. Pang and G. Hu, “Randomized gradient-free distributed optimization methods for a multiagent system with unknown cost function,” IEEE Trans. Automatic Control, vol. 65, no. 1, pp. 333–340, 2020. doi: 10.1109/TAC.2019.2914025
    [39]
    W. Ai, W. Chen, and J. Xie, “A general framework for population-based distributed optimization over networks,” Information Sciences, vol. 418–419, pp. 136–152, Dec. 2017.
    [40]
    Y. Wakasa and S. Nakaya, “Distributed particle swarm optimization using an average consensus algorithm,” in 2015 54th IEEE Conf. on Decision and Control (CDC), 2015, pp. 2661–2666.
    [41]
    M. K. Jalloul and M. A. Al-Alaoui, “A distributed particle swarm optimization algorithm for block motion estimation using the strategies of diffusion adaptation,” in 2015 Int. Symposium on Signals, Circuits and Systems (ISSCS), Jul. 2015, pp. 1–4.
    [42]
    Q. Yang, W.-N. Chen, T. Gu, H. Zhang, H. Yuan, S. Kwong, and J. Zhang, “A distributed swarm optimizer with adaptive communication for large-scale optimization,” IEEE Trans. Cybernetics, vol. 50, no. 7, pp. 3393–3408, 2020. doi: 10.1109/TCYB.2019.2904543
    [43]
    Y.-H. Jia, W.-N. Chen, T. Gu, H. Zhang, H.-Q. Yuan, S. Kwong, and J. Zhang, “Distributed cooperative co-evolution with adaptive computing resource allocation for large scale optimization,” IEEE Trans. Evolutionary Computation, vol. 23, no. 2, pp. 188–202, 2019. doi: 10.1109/TEVC.2018.2817889
    [44]
    A. Song, W.-N. Chen, Y.-J. Gong, X. Luo, and J. Zhang, “A divide-and-conquer evolutionary algorithm for large-scale virtual network embedding,” IEEE Trans. Evolutionary Computation, vol. 24, no. 3, pp. 566–580, 2020.
    [45]
    H. Zhao, J. Yan, X. Luo, and X. Gua, “Privacy preserving solution for the asynchronous localization of underwater sensor networks,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 6, pp. 1511–1527, 2020. doi: 10.1109/JAS.2020.1003312
    [46]
    C. He, Y. Wang, W. Yu, and L. Song, “Underwater target localization and synchronization for a distributed simo sonar with an isogradient ssp and uncertainties in receiver locations,” Sensors, vol. 19, no. 9, p. 1976, 2019. doi: 10.3390/s19091976
    [47]
    M. Brambilla, M. Nicoli, G. Soatti, and F. Deflorio, “Augmenting vehicle localization by cooperative sensing of the driving environment: Insight on data association in urban traffic scenarios,” IEEE Trans. Intelligent Transportation Systems, vol. 21, no. 4, pp. 1646–1663, 2019.
    [48]
    Y. Lei, Y. Wu, and A. J. K. Chowdhury, “Multi-target tracking algorithm in intelligent transportation based on wireless sensor network,” Open Physics, vol. 16, no. 1, pp. 1000–1008, 2018. doi: 10.1515/phys-2018-0121
    [49]
    H. Dai, Z.-m. Zhu, and X.-F. Gu, “Multi-target indoor localization and tracking on video monitoring system in a wireless sensor network,” Journal of Network and Computer Applications, vol. 36, no. 1, pp. 228–234, 2013. doi: 10.1016/j.jnca.2012.08.013
    [50]
    S. Xu, K. Doğançay, and H. Hmam, “Distributed pseudolinear estimation and UAV path optimization for 3D AOA target tracking,” Signal Processing, vol. 133, no. September 2016, pp. 64–78, 2017.
    [51]
    T.-Y. Chen, W.-N. Chen, F.-F. Wei, X.-M. Hu, and J. Zhang, “Multi-agent swarm optimization with adaptive internal and external learning for complex consensus-based distributed optimization,” IEEE Trans. Evolutionary Computation, pp. 1–1, 2024.
    [52]
    X. Yi, S. Zhang, T. Yang, and K. H. Johansson, “Zeroth-order algorithms for stochastic distributed nonconvex optimization,” Automatica, vol. 142, p. 110353, 2022. doi: 10.1016/j.automatica.2022.110353
    [53]
    V. Krishnamurthy, K. Pattanayak, S. Gogineni, B. Kang, and M. Rangaswamy, “Adversarial Radar Inference: Inverse Tracking, Identifying Cognition, and Designing Smart Interference,” IEEE Trans. Aerospace and Electronic Systems, vol. 57, no. 4, pp. 2067–2081, 2021. doi: 10.1109/TAES.2021.3090901
    [54]
    D. Ciuonzo, P. K. Willett, and Y. Bar-Shalom, “Tracking the tracker from its passive sonar ML-PDA estimates,” IEEE Trans. Aerospace and Electronic Systems, vol. 50, no. 1, pp. 573–590, 2014. doi: 10.1109/TAES.2013.120407
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(4)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (26) PDF downloads(7) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return