Volume 31 Issue 2
Mar.  2022
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WANG Xiaoli, XIE Weixin, LI Liangqun. Labeled Multi-Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model[J]. Chinese Journal of Electronics, 2022, 31(2): 227-239. doi: 10.1049/cje.2020.00.156
Citation: WANG Xiaoli, XIE Weixin, LI Liangqun. Labeled Multi-Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model[J]. Chinese Journal of Electronics, 2022, 31(2): 227-239. doi: 10.1049/cje.2020.00.156

Labeled Multi-Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model

doi: 10.1049/cje.2020.00.156
Funds:  This work was supported in part by the National Natural Science Foundation of China (62171287, 61773267), the Major Scientific and Technological Project of Guangdong Province (2017B030308006), the Major Program for Tackling Key Problems of Guangzhou City, China (201704020144), and Science & Technology Program of Shenzhen (JCYJ20190808120417257)
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  • Author Bio:

    (corresponding author) was born in 1992. She is a Lecturer in College of Electronics and Information, GuangDong Polytechnic Normal University. She received the Ph.D. degree in College of Information Engineering of Shenzhen University in 2021. Her research interests include multisensor information fusion and target tracking. (Email: xlwang@szu.edu.cn)

    was born in 1941. He is a Professor and Doctoral Tutor of School of Information Engineering, Shenzhen University. His research interests include radar target recognition, multisensor information fusion, fuzzy information processing, and image processing

    was born in 1979. He is a Professor of School of Information Engineering, Shenzhen University. His research interests include multi-sensor information fusion and target tracking. (Email: lqli@szu.edu.cn)

  • Received Date: 2020-06-01
  • Accepted Date: 2021-10-15
  • Available Online: 2021-12-02
  • Publish Date: 2022-03-05
  • Aiming at the problem that the existing labeled multi-Bernoulli (LMB) method has a single and fixed model set, an LMB maneuvering target tracking algorithm via Takagi-Sugeno-Kang (TSK) iterative regression multiple model is proposed. In the TSK iterative regression modeling, the feature information of the targets is analyzed and represented by multiple semantic fuzzy sets. Then the state is expanded to introduce model information, thereby the adaptive multi-model idea is incorporated into the framework of the LMB method to solve the uncertain maneuverability of moving targets. Finally, the simulation results show that the proposed algorithm can effectively achieve maneuvering target tracking in the nonlinear system.
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  • [1]
    Melyantono S. E., Susetya H., Widayani P., et al., “The rabies distribution pattern on dogs using average nearest neighbor analysis approach in the Karangasem District, Bali, Indonesia, in 2019,” Veterinary World, vol.14, no.3, pp.614–624, 2021. doi: 10.14202/vetworld.2021.614-624
    [2]
    Cheng L, Li Y, Xue M, et al., “An indoor localization algorithm based on modified joint probabilistic data association for wireless sensor network,” IEEE Transactions on Industrial Informatics, vol.17, no.1, pp.63–72, 2020.
    [3]
    Blackman S, “Multiple hypothesis tracking for multi-target tracking,” IEEE Transactions on Aerospace and Electronic Systems, vol.40, no.1, pp.5–18, 2004.
    [4]
    Brekke E and Chitre M, “Relationship between finite set statistics and the multiple hypothesis tracker,” IEEE Transactions on Aerospace & Electronic Systems, vol.54, no.4, pp.1902–1917, 2018.
    [5]
    Mahler R, “Multitarget bayes filtering via first-order multitarget moments,” IEEE Transactions on Aerospace and Electronic Systems, vol.39, no.4, pp.1152–1178, 2003. doi: 10.1109/TAES.2003.1261119
    [6]
    Bordonaro S V, Willett P, Bar-Shalom Y, et al., “Converted measurement sigma point Kalman filter for bistatic sonar and radar tracking,” IEEE Transactions on Aerospace and Electronic Systems, vol.55, no.1, pp.147–159, 2019. doi: 10.1109/TAES.2018.2849179
    [7]
    Son H S, “Smart tracking algorithm for multi-static sonar based on expectation maximisation,” IET Radar Sonar Navigation, vol.14, no.10, pp.1624–1630, 2020. doi: 10.1049/iet-rsn.2020.0127
    [8]
    Ma W K, Vo Ba-Ngu, Singh S, et al., “Tracking an unknown time-varying number of speakers using TDOA measurements a random finite set approach,” IEEE Transactions on Signal Processing, vol.54, no.9, pp.3291–3304, 2006. doi: 10.1109/TSP.2006.877658
    [9]
    Maggio E, Taj M, and Cavallaro A, “Efficient multitarget visual tracking using random finite sets,” IEEE Trans. on Circuits and Systems for Video Technology, vol.18, no.8, pp.1016–1027, 2008. doi: 10.1109/TCSVT.2008.928221
    [10]
    Zhou X, Li Y F, and He B, “Entropy distribution and coverage rate-based birth intensity estimation in GM-PHD filter for multi-target visual tracking,” Signal processing, vol.94, no.1, pp.650–660, 2014.
    [11]
    Pollard E, Plyer A, and Pannetier B, “GM-PHD filters for multi-object tracking in uncalibrated aerial videos,” 2009 12th International Conference on Information Fusion, IEEE, Seattle, WA, USA, pp.1171–1178, 2009.
    [12]
    Mullane J, Vo Ba-Ngu, Adams M D, et al., “A random finite set approach to Bayesian SLAM,” IEEE Transactions on Robotics, vol.27, no.2, pp.268–282, 2011. doi: 10.1109/TRO.2010.2101370
    [13]
    Vo B T, Vo B N, and Cantoni A, “The cardinality balanced multi-target multi-Bernoulli filter and its implementations,” IEEE Transactions on Signal Processing, vol.57, no.2, pp.409–423, 2009. doi: 10.1109/TSP.2008.2007924
    [14]
    Vo B T and Vo B N, “Labeled random finite sets and multi-object conjugate priors,” IEEE Transactions on Signal Processing, vol.61, no.13, pp.3460–3475, 2013. doi: 10.1109/TSP.2013.2259822
    [15]
    Vo B N, Vo B T, and Phung D, “Labeled random finite sets and the Bayes multi-target tracking filter,” IEEE Transactions on Signal Processing, vol.62, no.24, pp.6554–6567, 2014. doi: 10.1109/TSP.2014.2364014
    [16]
    Reuter S, Vo B T, Vo B N, et al., “The labeled multi-Bernoulli filter,” IEEE Transactions on Signal Processing, vol.62, no.12, pp.3246–3260, 2014. doi: 10.1109/TSP.2014.2323064
    [17]
    Vo B N, Vo B T, and Hoang H G, “An efficient implementation of the generalized labeled multi-Bernoulli filter,” IEEE Transactions on Signal Processing, vol.65, no.8, pp.1975–1987, 2017.
    [18]
    Dunne D and Kirubarajan T, “Multiple model multi-Bernoulli filters for manoeuvering targets,” IEEE Transactions on Aerospace & Electronic Systems, vol.49, no.4, pp.2679–2692, 2013.
    [19]
    Pasha S A, Vo B N, Tuan H D, et al., “A Gaussian mixture PHD filter for jump Markov system models,” IEEE Transactions on Aerospace & Electronic Systems, vol.45, no.3, pp.919–936, 2009.
    [20]
    Ji H B, Yang J L, and Ge H W, “Multi-model particle cardinality-balanced multi-target multi-Bernoulli algorithm for multiple manoeuvring target tracking,” IET Radar Sonar & Navigation, vol.7, no.2, pp.101–112, 2013.
    [21]
    QIU Hao, HUANG Gaoming, and ZUO Wei, “Multiple model labeled multi-Bernoulli filter for maneuvering target tracking,” Systems Engineering & Electronics, vol.37, no.12, pp.2683–2688, 2015.
    [22]
    Cament L, Correa J, Adams M, et al., “The histogram Poisson, labeled multi-Bernoulli multi-target tracking filter,” Signal Processing, vol.176, no.3, pp.107714–107728, 2020.
    [23]
    Chang C W and Tao C W, “A novel approach to implement Takagi-Sugeno fuzzy models,” IEEE Transactions on Cybernetics, vol.47, no.9, pp.2353–2361, 2017. doi: 10.1109/TCYB.2017.2701900
    [24]
    Li Y and Li Y, “Robust L1 output tracking control for uncertain networked control systems described by T-S fuzzy model with distributed delays,” International Journal of Systems Science, vol.48, no.15, pp.1–9, 2017.
    [25]
    Xie X, Lin L, and Zhong S, “Process Takagi–Sugeno model: A novel approach for handling continuous input and output functions and its application to time series prediction,” Knowledge-Based Systems, vol.63, no.3, pp.46–58, 2014.
    [26]
    Li L Q, Wang X L, Xie W X, et al., “A novel recursive T-S fuzzy semantic modeling approach for discrete state-space systems,” Neurocomputing, vol.340, no.5, pp.222–232, 2019.
    [27]
    Wang X L, Li L Q, and Xie W X, “A novel FEM based T-S fuzzy particle filtering for bearings-only maneuvering target tracking,” Sensors, vol.19, no.9, pp.2208–2228, 2019. doi: 10.3390/s19092208
    [28]
    Wang X L, Xie W X, and Li L Q, “Interacting T-S fuzzy particle filter algorithm for transfer probability matrix of adaptive online estimation model,” Digital Signal Processing, vol.110, no.5, pp.102944–102956, 2020.
    [29]
    Wang X L, Xie W X, and Li L Q, “Structure identification of recursive TSK particle filtering via type-2 intuitionistic fuzzy decision,” International Journal of Fuzzy Systems, vol.23, no.5, pp.1294–1312, 2021. doi: 10.1007/s40815-020-01021-6
    [30]
    Jadbabaie A, “A reduction in conservatism in stability and L_2 gain analysis of Takagi-Sugeno fuzzy systems via linear matrix inequalities,” IFAC Proceedings Volumes, vol.32, no.2, pp.5451–5455, 1999. doi: 10.1016/S1474-6670(17)56928-1
    [31]
    Xie X, Yue D, Zhang H, et al., “Control synthesis of discrete-time T-S fuzzy systems: Reducing the conservatism whilst alleviating the computational burden,” IEEE Transactions on Cybernetics, vol.47, no.9, pp.2480–2491, 2017. doi: 10.1109/TCYB.2016.2582747
    [32]
    Tuyet Vu and Rob Evans, “Optimal subpattern assignment metric for multiple tracks (OSPAMT metric),” Signal Processing, vol.4, no.16, pp.1–15, 2018.
    [33]
    Michael Beard, Ba Tuong Vo, and Ba-Ngu Vo, “OSPA(2): Using the OSPA metric to evaluate multi-target tracking performance,” 2017 International Conference on Control, Automation and Information Sciences (ICCAIS), pp.86–91, 2017.
    [34]
    Liu Z X and Huang B J, “The labeled multi-Bernoulli filter for jump Markov systems under glint noise,” IEEE Access, vol.7, no.3, pp.92322–92328, 2019.
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