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Performance bounds of distributed adaptive filters with cooperative correlated signals

合作相关信号下分布式自适应滤波的性能上界

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Abstract

In this paper, we studied the least mean-square-based distributed adaptive filters, aiming at collectively estimating a sequence of unknown signals (or time-varying parameters) from a set of noisy measurements obtained through distributed sensors. The main contribution of this paper to relevant literature is that under a general stochastic cooperative signal condition, stability and performance bounds are established for distributed filters with general connected networks without stationarity or independency assumptions imposed on the regression signals.

创新点

本文考虑一类基于最小均方算法(LMS)的分布式自适应滤波问题, 在传感器网络的连通条件和回归信号的联合激励条件下, 证明了算法的稳定性并给出了跟踪误差方差的上界。我们对回归向量不需要传统的独立性或平稳性等苛刻假设, 使得本文所建立的理论对反馈系统的应用成为可能。

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References

  1. Macchi O. Adaptative Processing: the Least Mean Squares Approach With Applications in Transmission. New York: John Wiley & Sons, Ltd., 1995

    MATH  Google Scholar 

  2. Sayed A H. Fundamentals of Adaptive Filtering. Hoboken: Wiley-IEEE Press, 2003

    Google Scholar 

  3. Haykin S S. Adaptive Filter Theory. Englewood Cliffs: Prentice Hall, 2008

    MATH  Google Scholar 

  4. Akyildiz I, Su W, Sankarasubramaniam Y, et al. Wireless sensor networks: a survey. Comput Netw, 2002, 38: 393–422

    Article  Google Scholar 

  5. Zhang Q, Zhang J F. Distributed parameter estimation over unreliable networks with Markovian switching topologies. IEEE Trans Automat Control, 2012, 57: 2545–2560

    Article  MathSciNet  Google Scholar 

  6. Kar S, Moura J M F, Poor H V. Distributed linear parameter estimation: asymptotically efficient adaptive strategies. SIAM J Control Optim, 2013, 51: 2200–2229

    Article  MathSciNet  MATH  Google Scholar 

  7. Kar S, Moura J M F. Convergence rate analysis of distributed gossip (linear parameter) estimation: fundamental limits and tradeoffs. IEEE J Sel Top Signal Process, 2011, 5: 674–690

    Article  Google Scholar 

  8. Chen W S, Wen C Y, Hua S Y, et al. Distributed cooperative adaptive identification and control for a group of continuous-time systems with a cooperative PE condition via consensus. IEEE Trans Automat Control, 2014, 59: 91–106

    Article  MathSciNet  Google Scholar 

  9. Cattivelli F S, Sayed A H. Diffusion LMS Strategies for distributed estimation. IEEE Trans Signal Process, 2010, 55: 2069–2084

    MathSciNet  Google Scholar 

  10. Schizas I D, Mateos G, Giannakis G B. Distributed LMS for consensus-based in-network adaptive processing. IEEE Trans Signal Process, 2009, 8: 2365–2381

    Article  MathSciNet  Google Scholar 

  11. Stankovic S S, Stankovic M S, Stipanovic D M. Decentralized parameter estimation by consensus based stochastic approximation. IEEE Trans Automat Control, 2014, 56: 531–543

    Article  MathSciNet  MATH  Google Scholar 

  12. Sayed A H. Adaptive networks. Proc IEEE, 2014, 102: 460–497

    Article  Google Scholar 

  13. Sayed A H. Adaptation, learning, and optimization over networks. Found Trends Mach Learn, 2014, 7: 311–801

    Article  MATH  Google Scholar 

  14. Guo L, Chen H F. Identification and Stochastic Adaptive Control. Boston: Birkhauser, 1991

    MATH  Google Scholar 

  15. Sayed A H, Lopes C G. Adaptive processing over distributed networks. IEICE Trans Fund Electron Commun Comput Sci, 2007, E90-A: 1504–1510

    Article  Google Scholar 

  16. Guo L, Ljung L. Performance analysis of general tracking algorithms. IEEE Trans Automat Control, 1995, 40: 1388–1402

    Article  MathSciNet  MATH  Google Scholar 

  17. Guo L. Stability of recursive stochastic tracking algorithms. SIAM J Control Optim, 1994, 32: 1195–1225

    Article  MathSciNet  MATH  Google Scholar 

  18. Guo L, Ljung L. Exponential stability of general tracking algorithms. IEEE Trans Automat Control, 1995, 40: 1376–1387

    Article  MathSciNet  MATH  Google Scholar 

  19. Chen C, Liu Z X, Guo L. Stability of diffusion adaptive filters. In: Proceedings of the 19th World Congress of the International Federation of Automatic Control, Cape Town, 2014. 10409–10414

    Google Scholar 

  20. Chen C, Liu Z X, Guo L. Performance analysis of distributed adaptive filters. Commun Inform Syst, 2015, 15: 453–476

    Article  MathSciNet  MATH  Google Scholar 

  21. Xue M, Roy S. Kronecker products of defective matrices: some spectral properties and their implications on observability. In: Proceedings of the 2012 American Control Conference, Montr´eal, 2012. 5202–5207

    Google Scholar 

  22. Saloff-Coste L, Zúniga J. Convergence of some time inhomogeneous Markov chains via spectral techniques. Stoch Proc Appl, 2007, 117: 961–979

    Article  MathSciNet  MATH  Google Scholar 

  23. Carli R, Chiuso A, Schenato L, et al. Distributed Kalman filtering based on consensus strategies. IEEE J Sel Areas Commun, 2008, 26: 622–633

    Article  Google Scholar 

  24. Khan U A, Moura J M F. Distributing the Kalman filter for large-scale systems. IEEE Trans Signal Process, 2008, 56: 4919–4935

    Article  MathSciNet  Google Scholar 

  25. Olfati-Saber R. Distributed Kalman filtering for sensor networks. In: Proceedings of the 46th Conference on Decision Control, New Orleans, 2007. 5492–5498

    Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61273221) and National Basic Research Program of China (973) (Grant No. 2014CB845302).

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Authors and Affiliations

  1. Shannon Cognitive Computing Laboratory, 2012 Lab, Huawei Technologies Co. Ltd., Beijing, 100085, China

    Chen Chen

  2. Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Zhixin Liu & Lei Guo

Authors
  1. Chen Chen
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  2. Zhixin Liu
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  3. Lei Guo
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Correspondence to Lei Guo.

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Chen, C., Liu, Z. & Guo, L. Performance bounds of distributed adaptive filters with cooperative correlated signals. Sci. China Inf. Sci. 59, 112202 (2016). https://doi.org/10.1007/s11432-016-0050-9

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  • DOI: https://doi.org/10.1007/s11432-016-0050-9

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