logo

SCIENCE CHINA Information Sciences, Volume 60 , Issue 3 : 032204(2017) https://doi.org/10.1007/s11432-016-0284-2

Distributed incremental bias-compensated RLS estimation over multi-agent networks

More info
  • ReceivedJul 11, 2016
  • AcceptedAug 25, 2016
  • PublishedFeb 7, 2016

Abstract

In this paper, we study the problem of distributed bias-compensated recursive least-squares (BC-RLS) estimation over multi-agent networks, where the agents collaborate to estimate a common parameter of interest. We consider the situation where both input and output of each agent are corrupted by unknown additive noise. Under this condition, traditional recursive least-squares (RLS) estimator is biased, and the bias is induced by the input noise variance. When the input noise variance is available, the effect of the noise-induced bias can be removed at the expense of an increase in estimation variance. Fortunately, it has been illustrated that distributed collaboration between agents can effectively reduce the variance and can improve the stability of the estimator. Therefore, a distributed incremental BC-RLS algorithm and its simplified version are proposed in this paper. The proposed algorithms can collaboratively obtain the estimates of the unknown input noise variance and remove the effect of the noise-induced bias. Then consistent estimation of the unknown parameter can be achieved in an incremental fashion. Simulation results show that the incremental BC-RLS solutions outperform existing solutions in some enlightening ways.


Funded by

National Natural Science Foundation of China(61421001)


Acknowledgment

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 61421001).


References

[1] Sayed A H. Adaptation, learning, and optimization over networks. Found Trends March Learn, 2014, 7: 312-318 Google Scholar

[2] Cattivelli F S, Sayed A H. Analysis of spatial and incremental LMS processing for distributed estimation. IEEE Trans Signal Process, 2011, 59: 1465-1480 CrossRef Google Scholar

[3] Estrin D, Girod L, Pottie G, et al. Instrumenting the world with wireless sensor networks. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Salt Lake City, 2001. 2033--2036. Google Scholar

[4] Sayed A H, Lopes C G. Distributed processing over adaptive networks. In: Proceedings of the 9th International Symposium on Signal Processing and its Applications (ISSPA), Sharjah, 2007. 1--3. Google Scholar

[5] Brust M R, Akbas M I, Turgut D. Multi-hop localization system for environmental monitoring in wireless sensor and actor networks. Concurrency Comput Pract Exper, 2013, 25: 701-717 CrossRef Google Scholar

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

[7] Khalili A, Tinati M A, Rastegarnia A. Analysis of incremental RLS adaptive networks with noisy links. IEICE Electron Express, 2011, 8: 623-628 CrossRef Google Scholar

[8] Bogdanovic N, Plata-Chaves J, Berberidis K. Distributed incremental-based LMS for node-specific parameter estimation over adaptive networks. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, 2013. 5425--5429. Google Scholar

[9] Plata -Chaves J, Bogdanovic N, Berberidis K. Distributed incremental-based RLS for node-specific parameter estimation over adaptive networks. In: Proceedings of the 21st European Signal Processing Conference (EUSIPCO), Marrakech, 2013. 1--5. Google Scholar

[10] Khalili A, Rastegarnia A, Bazzi W M, et al. Derivation and analysis of incremental augmented complex least mean square algorithm. IET Signal Process, 2015, 9: 312-319 CrossRef Google Scholar

[11] Bogdanovic N, Plata -Chaves J, Berberidis K. Distributed incremental-based LMS for node-specific adaptive parameter estimation. IEEE Trans Signal Process, 2014, 62: 5382-5397 CrossRef Google Scholar

[12] Khalili A, Rastegarnia A, Bazzi W M, et al. Tracking performance of incremental augmented complex least mean square adaptive network in the presence of model non-stationarity. IET Signal Process, 2016, 10: 798-804 CrossRef Google Scholar

[13] Rastegarnia A, Tinati M A, Khalili A. Performance analysis of quantized incremental LMS algorithm for distributed adaptive estimation. Signal Process, 2010, 90: 2621-2627 CrossRef Google Scholar

[14] Rastegarnia A, Khalili A. Incorporating observation quality information into the incremental LMS adaptive networks. Arab J Sci Eng, 2014, 39: 987-995 CrossRef Google Scholar

[15] Tu S Y, Sayed A H. Diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks. IEEE Trans Signal Process, 2012, 60: 6217-6234 CrossRef Google Scholar

[16] Sayed A H, Lopes C G. Adaptive processing over distributed networks. IEICE Trans Fund Electron Commun Comput Sci, 2007, 90: 1504-1510 Google Scholar

[17] 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

[18] Abdolee R, Champagne B, Sayed A H. A diffusion LMS strategy for parameter estimation in noisy regressor applications. In: Proceedings of the 20th European Signal Processing Conference (EUSIPCO), Bucharest, 2012. 749--753. Google Scholar

[19] Sayed A H, Tu S Y, Chen J S, et al. Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior. IEEE Signal Process Mag, 2013, 30: 155-171 CrossRef Google Scholar

[20] Chen J S, Sayed A H. Distributed Pareto optimization via diffusion strategies. IEEE J Sel Top Sign Proces, 2013, 7: 205-220 CrossRef Google Scholar

[21] Sayed A H. Diffusion adaptation over networks. In: Academic Press Library in Signal Processing. Boston: Academic Press Elsevier, 2014. 323--454. Google Scholar

[22] Cattivelli F S, Sayed A H. Diffusion LMS strategies for distributed estimation. IEEE Trans Signal Process, 2010, 58: 1035-1048 CrossRef Google Scholar

[23] Bertrand A, Moonen M, Sayed A H. Diffusion-based bias-compensated RLS for distributed estimation over adaptive sensor networks. In: Proceedings of the 19th European Signal Processing Conference (EUSIPCO), Barcelona, 2011. 1025--1029. Google Scholar

[24] Bertrand A, Moonen M, Sayed A H. Diffusion bias-compensated RLS estimation over adaptive networks. IEEE Trans Signal Process, 2011, 59: 5212-5224 CrossRef Google Scholar

[25] Zhao X C, Tu S Y, Sayed A H. Diffusion adaptation over networks under imperfect information exchange and non-stationary data. IEEE Trans Signal Process, 2012, 60: 3460-3475 CrossRef Google Scholar

[26] Jia L J, Tao R, Wang Y, et al. Forward/backward prediction solution for adaptive noisy FIR filtering. Sci China Ser F-Inf Sci, 2009, 52: 1007-1014 CrossRef Google Scholar

[27] Ljung L. System Identification. Boston: Birkhauser, 1998. 163--173. Google Scholar

[28] Haykin S. Adaptive Filter Theory. 4th ed. Upper Saddle River: Prentice Hall, 2002. 442--444. Google Scholar

Copyright 2020  CHINA SCIENCE PUBLISHING & MEDIA LTD.  中国科技出版传媒股份有限公司  版权所有

京ICP备14028887号-23       京公网安备11010102003388号