logo

SCIENCE CHINA Information Sciences, Volume 59, Issue 11: 112202(2016) https://doi.org/10.1007/s11432-016-0050-9

Performance bounds of distributed adaptive filters with cooperative correlated signals

More info
  • ReceivedJan 18, 2016
  • AcceptedFeb 25, 2016
  • PublishedOct 17, 2016

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.


Funded by

"source" : null , "contract" : "2014CB845302"}]

National Natural Science Foundation of China(61273221)

National Basic Research Program of China(973)


Acknowledgment

Acknowledgments

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).


References

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

[4] Akyildiz I, Su W, Sankarasubramaniam Y, et al. Wireless sensor networks: a survey. Comput Netw, 2002, 38: 393-422 CrossRef 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 CrossRef 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 CrossRef 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 CrossRef 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 CrossRef Google Scholar

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

[12] Sayed A H. Adaptive networks. Proc IEEE, 2014, 102: 460-497 CrossRef Google Scholar

[13] Sayed A H. Adaptation, learning, and optimization over networks. Found Trends Mach Learn, 2014, 7: 311-801 CrossRef Google Scholar

[14] Guo L, Chen H F. Identification and Stochastic Adaptive Control. Boston: Birkhauser, 1991. 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 CrossRef Google Scholar

[16] Guo L, Ljung L. Performance analysis of general tracking algorithms. IEEE Trans Automat Control, 1995, 40: 1388-1402 CrossRef Google Scholar

[17] Guo L. Stability of recursive stochastic tracking algorithms. SIAM J Control Optim, 1994, 32: 1195-1225 CrossRef Google Scholar

[18] Guo L, Ljung L. Exponential stability of general tracking algorithms. IEEE Trans Automat Control, 1995, 40: 1376-1387 CrossRef 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 CrossRef 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}, Montréal, 2012. 5202--5207. Google Scholar

[22] Saloff-Coste L, Zú\ {n}iga J. Convergence of some time inhomogeneous Markov chains via spectral techniques. Stoch Proc Appl, 2007, 117: 961-979 CrossRef 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 CrossRef 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 CrossRef 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 \iffalse. Google Scholar

[26] Lopes C G, Sayed A H. Incremental adaptive strategies over distributed networks. IEEE Trans Signal Process, 2007, 55: 4064-4077 CrossRef Google Scholar

[27] Cattivelli F S, Lopes C G, Sayed A H. Diffusion recursive least-squares for distributed estimation over adaptive networks. IEEE Trans Signal Process, 2008, 56: 1865-1877 CrossRef Google Scholar

[28] Mateos G, Schizas I D, Giannakis G B. Distributed recursive least-squares for consensus-based in-network adaptive estimation. IEEE Trans Signal Process, 2009, 57: 4583-fi CrossRef Google Scholar

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1