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Distributed Nonlinear Estimation: A Recursive Optimization Approach

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Abstract

This paper is concerned with the distributed estimation problem for nonlinear systems with unknown but bounded noises, where the bounds of noises are also unknown. By modeling a new distributed estimation error system including the bias among local estimation errors, a bounded recursive optimization scheme is constructed to determine the matrix gains of distributed estimator. Notice that the constructed optimization problem for each local estimator, which can be easily solved by the existing software applications, is only dependent on the itself information and its neighboring information, and thus the design of distributed estimator and the determination of estimator gains are all fully distributed. Finally, a range-only target tracking system is employed to show the effectiveness and advantages of the proposed methods.

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The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Y. Bar-Shalom, X.R. Li, T. Kirubarajan, Estimation with Applications to Tracking and Navigation (Wiley, New York, 2001)

    Book  Google Scholar 

  2. S. Battilotti, M. Mekhail, Distributed estimation for nonlinear systems. Automatica 107, 562–573 (2019)

    Article  MathSciNet  Google Scholar 

  3. S.P. Boyd, L.E. Ghaoui, E. Feron, V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory (SIAM, Philadelphia, 1994)

    Book  Google Scholar 

  4. M.G.S. Bruno, S.S. Dias, A Bayesian interpretation of distributed diffusion filtering algorithms [Lecture Notes]. IEEE Signal Process. Mag. 35, 118–123 (2018)

    Article  Google Scholar 

  5. S. Chen, B. Chen, F. Shi, Distributed fault-tolerant consensus protocol for fuzzy multi-agent systems. Circuits Syst. Signal Process. 38, 611–624 (2019)

    Article  MathSciNet  Google Scholar 

  6. B. Chen, G. Hu, Nonliear state estimation under bounded noises. Automatica 98, 15–168 (2018)

    Google Scholar 

  7. B. Chen, G. Hu, D.W.C. Ho, L. Yu, A new approach to linear/nonlinear distributed fusion estimation problem. IEEE Trans. Autom. Control 64, 1301–1308 (2019)

    Article  MathSciNet  Google Scholar 

  8. S. Chen, Y. Liu, Robust distributed parameter estimation of nonlinear systems with missing data over networks. IEEE Trans. Aerosp. Electron. Syst. 56, 2228–2244 (2020)

    Article  Google Scholar 

  9. J. Ding, J. Xiao, Y. Zhang, M.N. Iqbal, Distributed state estimation for discrete-time nonlinear system with unknown inputs. Circuits Systems Signal Process. 33, 3421–3441 (2014)

    Article  Google Scholar 

  10. H. Dong, Z. Wang, H. Gao, Distributed \(H_{\infty }\) filtering for a class of Markovian jump nonlinear time-delay systems over lossy sensor networks. IEEE Trans. Ind. Electron. 60, 4665–4672 (2013)

    Article  Google Scholar 

  11. P. Duan, Z. Duan, Y. Lv, G. Chen, Distributed finite-horizon extended Kalman filtering for uncertain nonlinear systems. IEEE Trans. Cybernet. (2019). https://doi.org/10.1109/TCYB.2019.2919919

    Article  Google Scholar 

  12. W. Li, Y. Jia, Consensus-based distributed multiple model UKF for jump Markov nonlinear systems. IEEE Trans. Autom. Control 57, 227–233 (2012)

    Article  MathSciNet  Google Scholar 

  13. W. Li, Y. Jia, J. Du, Distributed extended Kalman filter with nonlinear consensus estimate. J. Franklin Inst. 354, 7983–7995 (2017)

    Article  MathSciNet  Google Scholar 

  14. C. Lin, C. Wu, Linear coherent distributed estimation with cluster-based sensor networks. IET Signal Proc. 6, 626–632 (2012)

    Article  MathSciNet  Google Scholar 

  15. A. Rastegarnia, Reduced-communication diffusion RLS for distributed estimation over multi-agent networks. IEEE Trans. Circuits Syst. II Express Briefs 67, 177–181 (2020)

    Article  Google Scholar 

  16. S.M. Shafiul Alam, B. Natarajan, A. Pahwa, S. Curto, Agent based state estimation in smart distribution grid. IEEE Latin Am. Trans. 13, 496–502 (2015)

    Article  Google Scholar 

  17. J. Shi, S. Liu, B. Chen, L. Yu, Distributed data-driven intrusion detection for sparse stealthy FDI attacks in smart grids. IEEE Trans. Circuits Syst. II Express Briefs (2020). https://doi.org/10.1109/TCSII.2020.3020139

    Article  Google Scholar 

  18. X. Wang, G. Yang, Distributed event-triggered \(H_{\infty }\) filtering for discrete-time TCS fuzzy systems over sensor networks. IEEE Trans. Syst. Man Cyberneti. Syst. 50, 3269–3280 (2020)

    Article  Google Scholar 

  19. Y. Xu, R. Lu, P. Shi, H. Li, S. Xie, Finite-time distributed state estimation over sensor networks with round-robin protocol and fading channels. IEEE Trans. Cybernet. 48, 336–345 (2018)

    Article  Google Scholar 

  20. F. Yang, Q. Han, Y. Liu, Distributed \(H_{\infty }\) state estimation over a filtering network with time-varying and switching topology and partial information exchange. IEEE Trans. Cybernet. 49, 870–882 (2019)

    Article  Google Scholar 

  21. O. Younis, M. Krunz, S. Ramasubramanian, Node clustering in wireless sensor networks: recent developments and deployment challenges. IEEE Netw. 20, 20–25 (2006)

    Article  Google Scholar 

  22. H. Zayyani, Robust minimum disturbance diffusion LMS for distributed estimation. IEEE Trans. Circuits Syst.-II Express Briefs 68, 521–525 (2021)

    Article  Google Scholar 

  23. H. Zayyani, M. Korki, F. Marvasti, Bayesian hypothesis testing detector for one bit diffusion LMS with blind missing samples. Signal Process. 146, 61–65 (2018)

    Article  Google Scholar 

  24. D. Zhang, W. Cai, L. Xie, Q. Wang, Nonfragile distributed filtering for TCS fuzzy systems in sensor networks. IEEE Trans. Fuzzy Syst. 23, 1883–1890 (2015)

    Article  Google Scholar 

  25. D. Zhu, B. Chen, Z. Hong, L. Yu, Networked nonlinear fusion estimation under DoS attacks. IEEE Sens. J. (2020). https://doi.org/10.1109/JSEN.2020.3039918

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People’s Republic of China

    Yidi Teng & Shouzhao Sheng

  2. School of Electronic and Information Engineering, Changshu Institute of Technology, Changshu, 215500, People’s Republic of China

    Yidi Teng

  3. Department of Automation, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Bo Chen

Authors
  1. Yidi Teng
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  2. Bo Chen
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  3. Shouzhao Sheng
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Correspondence to Bo Chen.

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This work was supported by the Six-field Talent Peak Project of Jiangsu Province under Grant 2017-XYDXX-105.

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Teng, Y., Chen, B. & Sheng, S. Distributed Nonlinear Estimation: A Recursive Optimization Approach. Circuits Syst Signal Process 41, 2397–2410 (2022). https://doi.org/10.1007/s00034-021-01884-6

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  • DOI: https://doi.org/10.1007/s00034-021-01884-6

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