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Fuzzy Interacting Multiple Model H\(\infty\) Particle Filter Algorithm Based on Current Statistical Model

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Abstract

In this paper, fuzzy theory and interacting multiple model are introduced into H\(\infty\) filter-based particle filter to propose a new fuzzy interacting multiple model H\(\infty\) particle filter based on current statistical model. Each model uses H\(\infty\) particle filter algorithm for filtering, in which the current statistical model can describe the maneuver of target accurately and H\(\infty\) filter can deal with the nonlinear system effectively. Aiming at the problem of large amount of probability calculation in interacting multiple model by using combination calculation method, our approach calculates each model matching probability through the fuzzy theory, which can not only reduce the calculation amount, but also improve the state estimation accuracy to some extent. The simulation results show that the proposed algorithm can be more accurate and robust to track maneuvering target.

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References

  1. Radu, V., Yaakov, B., Peter, W.: Multiple-model estimators for tracking sharply maneuvering ground targets. IEEE Trans. Aerospace Electron. Syst. 54, 1404–1414 (2018)

    Article  Google Scholar 

  2. Hadaegh, M., Khaloozadeh, H.: Modified switched IMM estimator based on autoregressive extended Viterbi method for maneuvering target tracking. J. Syst. Eng.Electron. 29, 1142–1157 (2018)

    Google Scholar 

  3. Jiang, Z., Huynh, D.Q.: Multiple pedestrian tracking from monocular videos in an interacting multiple model framework. IEEE Trans. Image Process. 27, 1361–1375 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  4. Lee, D., Liu, C., Liao, Y., Hedrick, J.K.: Parallel interacting multiple model-based human motion prediction for motion planning of companion robots. IEEE Trans. Autom. Sci. Eng. 14, 52–61 (2017)

    Article  Google Scholar 

  5. Nesrine, A., Nidhal, C., Roman, S.: Souad Chebbi on the convergence of constrained particle filters. IEEE Signal Process. Lett. 24, 858–862 (2017)

    Article  Google Scholar 

  6. Branko, R., Jeremie, H., Sanjeev, A.: Robust target motion analysis using the possibility particle filter. IET Radar Sonar Navig. 13, 18–22 (2019)

    Article  Google Scholar 

  7. Yu, M., Gong, L., Oh, H., Chen, W., Chambers, J.: Multiple model ballistic missile tracking with state-dependent transitions and gaussian particle filtering. IEEE Trans. Aerospace Electron. Syst. 54, 1066–1081 (2018)

    Article  Google Scholar 

  8. Georgia, C., Xanthi, S.P., Costas, S.T., Petros, M.: Augmented human state estimation using interacting multiple model particle filters with probabilistic data association. IEEE Robot. Autom. Lett. 3, 1872–1879 (2018)

    Article  Google Scholar 

  9. Zhang, Y., Wang, S., Li, J.: Improved particle filtering techniques based on generalized interactive genetic algorithm. J. Syst. Eng. Electron. 27, 242–250 (2016)

    Google Scholar 

  10. Xiao, G., Li, K., Zhou, X., Li, K.: Efficient monochromatic and bichromatic probabilistic reverse top-k query processing for uncertain big data. J. Computer Syst. Sci. 89, 92–113 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  11. Xiao, G., Li, K., Li, K.: Reporting l most influential objects in uncertain databases based on probabilistic reverse top-k queries. Inf. Sci. 405, 207–226 (2017)

    Article  Google Scholar 

  12. Schwiegelshohn, F., Ossovski, E., Hbner, M.: A resampling method for parallel particle filter architectures. Microprocess. Microsyst. 47, 314–320 (2016)

    Article  Google Scholar 

  13. Xiao G., Li K., Chen Y., He W., Zomaya A., Li T.: A customized and accelerative SpMV framework for the Sunway TaihuLight. IEEE Trans. Parallel Distrib. Syst. https://doi.org/10.1109/TPDS.2019.2907537 (2019)

    Google Scholar 

  14. Bai A., Simmons R., Veloso, M.: Multi-object tracking and identification via particle filtering over sets. In: Proceedings of the 20th International Conference on Information Fusion, pp. 10–13 (2017)

  15. Wang, Q., Li, J., Zhang, M., Yang, C.: H-infinity filter based particle filter for maneuvering target tracking. Prog. Electromagn. Res. B 30, 103–116 (2011)

    Article  Google Scholar 

  16. Shi, P., Zhang, Y., Chadli, M., Agarwal, R.K.: Mixed H-infinity and passive filtering for discrete fuzzy neural networks with stochastic jumps and time delays. IEEE Trans. Neural Netw. Learn. Syst. 27, 903–909 (2016)

    Article  MathSciNet  Google Scholar 

  17. Yu, Q., Xiong, R., Lin, C., Shen, W., Deng, J.: Lithium-ion battery parameters and state-of-charge joint estimation based on h-infinity and unscented Kalman filters. IEEE Trans. Veh. Technol. 66, 8693–8701 (2017)

    Article  Google Scholar 

  18. Jia, S., Zhang, Y., Wang, G.: Highly maneuvering target tracking using multi-parameter fusion singer model. J. Syst. Eng. Electron. 28, 841–850 (2017)

    Article  Google Scholar 

  19. Pojala, C., Somnath, S.: Rough-set-theoretic fuzzy cues-based object tracking under improved particle filter framework. IEEE Trans. Fuzzy Syst. 24, 695–707 (2016)

    Article  Google Scholar 

  20. Cosme, L.B., Caminhas, W.M., D’Angelo, M.F., Palhares, R.M.: A novel fault-prognostic approach based on interacting multiple model filters and fuzzy systems. IEEE Trans. Ind. Electron. 66, 519–528 (2019)

    Article  Google Scholar 

  21. Li, L., Xie, W., Liu, Z.: Auxiliary truncated particle filtering with least-square method for bearings-only maneuvering target tracking. IEEE Trans. Aerospace Electron. Syst. 52, 2562–2567 (2016)

    Article  Google Scholar 

  22. Shi, X., Celik, N.: Relative entropy-based density selection in particle filtering for load demand forecast. IEEE Trans. Autom. Sci. Eng. 14, 946–954 (2017)

    Article  Google Scholar 

  23. Xiao, G., Wu, F., Zhou, X., Li, K.: Probabilistic top-k range query processing for uncertain databases. J. Intell. Fuzzy Syst. 31, 1109–1120 (2016)

    Article  MATH  Google Scholar 

  24. Li, K., Yang, W., Li, K.: Performance analysis and optimization for SpMV on GPU using probabilistic modeling. IEEE Trans. Parallel Distrib. Syst. 26, 196–205 (2015)

    Article  MathSciNet  Google Scholar 

  25. Li, K., Yang, W., Li, K.: Energy-efficient stochastic task scheduling on heterogeneous computing systems. IEEE Trans. Parallel Distrib. Syst. 25, 2867–2876 (2014)

    Article  Google Scholar 

  26. Chen, Y., Xiao, G., Yang, W.: Optimizing partitioned CSR-based SpGEMM on the Sunway TaihuLight. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04121-z

    Google Scholar 

  27. Chen, Y., Li, K., Yang, W., Xiao, G., Xie, X., Li, T.: Performance-aware model for sparse matrix-matrix multiplication on the Sunway TaihuLight supercomputer. IEEE Trans. Parallel Distrib. Syst. 30, 923–938 (2019)

    Article  Google Scholar 

  28. Li, K., Yang, Z., Xiao, G., Li, K.: Progressive approaches for Pareto optimal groups computation. IEEE Trans. Knowl. Data Eng. 31, 521–534 (2019)

    Article  Google Scholar 

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Acknowledgements

The work was supported by the Shenzhen Science and Technology Projects (Grant No. JCYJ20180306173210774) and by NSFC under Contract No. 61671397.

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Authors and Affiliations

  1. Department of Computer Science, Xiamen University, Xiamen, 361005, China

    Qicong Wang, Xiaoqiang Chen, Jin Li & Chong Zhao

  2. Shenzhen Research Institute, Xiamen University, Shenzhen, 518000, China

    Qicong Wang

  3. Xiamen City Management Administrative Enforcement Bureau, Xiamen, 361000, China

    Lin Zhang

  4. Department of Computing, Canterbury Christ Church University, Kent, CT1 1QU, UK

    Man Qi

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  1. Qicong Wang
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  2. Xiaoqiang Chen
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  3. Lin Zhang
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  4. Jin Li
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  5. Chong Zhao
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  6. Man Qi
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Correspondence to Qicong Wang.

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Wang, Q., Chen, X., Zhang, L. et al. Fuzzy Interacting Multiple Model H\(\infty\) Particle Filter Algorithm Based on Current Statistical Model. Int. J. Fuzzy Syst. 21, 1894–1905 (2019). https://doi.org/10.1007/s40815-019-00678-y

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  • DOI: https://doi.org/10.1007/s40815-019-00678-y

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