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A Class of Distributed Variable Structure Multiple Model Algorithm Based on Posterior Information of Information Matrix

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Abstract

The tracking of maneuvering targets in radar networking scenarios is studied in this paper. For the interacting multiple model algorithm and the expected-mode augmentation algorithm, the fixed base model set leads to a mismatch between the model set and the target motion mode, which causes the reduction on tracking accuracy. An adaptive grid-expected-mode augmentation variable structure multiple model algorithm is proposed. The adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model set. Furthermore, combining with the unscented information filtering, and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy, a distributed target tracking algorithm based on the posterior information of the information matrix is proposed. For the problem of filtering divergence while target is leaving radar surveillance area, a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.

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References

  1. MEHROTRA K, MAHAPATRA P R. A jerk model for tracking highly maneuvering targets [J]. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(4): 1094–1105.

    Article  Google Scholar 

  2. HAO Y L, MENG F B, WANG S X, et al. A new GMPHD filter algorithm for multiple maneuvering targets tracking [J]. Journal of Shanghai Jiaotong University, 2010, 44(7): 873–877 (in Chinese).

    Google Scholar 

  3. FOO P H. Combining the interacting multiple model method with particle filters for manoeuvring target tracking with a multistatic radar system [J]. IET Radar, Sonar & Navigation, 2011, 5(7): 697–706.

    Article  Google Scholar 

  4. DI Y, GU X H, LONG F, et al. An improved UPF-based interacting multiple model target tracking method for dynamic acoustic array [J]. Control and Decision, 2018, 33(2): 249–255 (in Chinese).

    Google Scholar 

  5. ZHAO G Q, YANG M, WANG B, et al. Mobile robot seamless localization based on smart device in indoor and outdoor environments [J]. Journal of Shanghai Jiao Tong University, 2018, 52(1): 13–19 (in Chinese).

    Google Scholar 

  6. LI X R, BAR-SHALOM Y. Multiple-model estimation with variable structure [J]. IEEE Transactions on Automatic Control, 1996, 41(4): 478–493.

    Article  MathSciNet  Google Scholar 

  7. XIAO S, LI Z H, TAN X S, et al. DG-VSMM tracking algorithm for near-space hypersonic-vehicle [J]. Journal of Ballistics, 2013, 25(2): 22–27 (in Chinese).

    Google Scholar 

  8. LI X, ZHANG Y. Multiple-model estimation with variable structure. V. Likely-model set algorithm [J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2): 448–466.

    Article  Google Scholar 

  9. DING Q X, LIANG G W, TIAN Y, et al. Adaptive variable structure multiple model filter for high maneuvering target tracking [C]//2010 International Conference on Computational and Information Sciences. Chengdu, China: IEEE, 2010: 289–292.

    Google Scholar 

  10. ZHANG M, CHEN W D. Variable structure multiple model particle filter for maneuvering radar target tracking [C]//2010 International Conference on Microwave and Millimeter Wave Technology. Chengdu, China: IEEE, 2010: 1754–1757.

    Google Scholar 

  11. LI X, JILKOV V, RU J. Multiple-model estimation with variable structure-part VI: Expected-mode augmentation [J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(3): 853–867.

    Article  Google Scholar 

  12. PAN M M. Research on adaptive algorithms for highly maneuvering target tracking [D]. Xi’an, China: Xidian University, 2019 (in Chinese).

    Google Scholar 

  13. SHAO K, LEI Y K. AGIMM tracking algorithm based on fuzzy logic and maneuvering detection[J]. Journal of Air Force Engineering University (Natural Science Edition), 2020, 21(4): 80–87 (in Chinese).

    MathSciNet  Google Scholar 

  14. FU H J, YU S J, JI F, et al. Variable structure interactive multiple model algorithm based on current statistical model[J]. Radio Engineering, 2020, 50(4): 318–322 (in Chinese).

    Google Scholar 

  15. DING Z R, LIU Y, LIU J, et al. Adaptive interacting multiple model algorithm based on information-weighted consensus for maneuvering target tracking [J]. Sensors, 2018, 18(7): 2012.

    Article  MathSciNet  Google Scholar 

  16. CHEN Y S, JILKOV V P, LI X R. Multilane-road target tracking using radar and image sensors [J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 65–80.

    Article  Google Scholar 

  17. LI W L, JIA Y M, DU J P, et al. Distributed multiple-model estimation for simultaneous localization and tracking with NLOS mitigation [J]. IEEE Transactions on Vehicular Technology, 2013, 62(6): 2824–2830.

    Article  Google Scholar 

  18. XIAO L, BOYD S, LALL S. A scheme for robust distributed sensor fusion based on average consensus [C]//Fourth International Symposium on Information Processing in Sensor Networks. Boise, ID, USA: IEEE, 2005: 63–70.

    Google Scholar 

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Authors and Affiliations

  1. Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yinghao Huang  (黄颖浩) & Yunze Cai  (蔡云泽)

  2. Department of Automation; Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yinghao Huang  (黄颖浩), Lixiu Yao  (姚莉秀) & Yunze Cai  (蔡云泽)

  3. Alibaba (China) Co., Ltd., Hangzhou, 311121, China

    Yi Wu  (吴 怡)

Authors
  1. Yinghao Huang  (黄颖浩)
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  2. Yi Wu  (吴 怡)
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  3. Lixiu Yao  (姚莉秀)
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  4. Yunze Cai  (蔡云泽)
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Corresponding author

Correspondence to Yunze Cai  (蔡云泽).

Additional information

Foundation item: the Joint Fund of Advanced Aerospace Manufacturing Technology Research (No. 2017-JCJQ-ZQ-031)

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Huang, Y., Wu, Y., Yao, L. et al. A Class of Distributed Variable Structure Multiple Model Algorithm Based on Posterior Information of Information Matrix. J. Shanghai Jiaotong Univ. (Sci.) 27, 671–679 (2022). https://doi.org/10.1007/s12204-022-2458-x

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  • DOI: https://doi.org/10.1007/s12204-022-2458-x

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