Abstract
In this paper, the distributed and recursive blind channel identification algorithms are proposed for single-input multi-output (SIMO) systems of sensor networks (both time-invariant and time-varying networks). At any time, each agent updates its estimate using the local observation and the information derived from its neighboring agents. The algorithms are based on the truncated stochastic approximation and their convergence is proved. A simulation example is presented and the computation results are shown to be consistent with theoretical analysis.
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This paper is dedicated to Professor T. J. Tarn on the occasion of his 80th birthday.
This work was supported by the National Key Basic Research Program of China (973 program, No. 2014CB845301), and the National Center for Mathematics and Interdisciplinary Science, Chinese Academy of Sciences.
Rui LIU received her B.Sc. degree in Statistics from Nankai University in 2014 and M.Sc. degree in Operations Research and Cybernetics from Academy of Mathematics and Systems Science, Chinese Academy of Sciences in 2017. Her research interests lie in distributed algorithms and stochastic approximation and its applications to systems, control, and signal processing.
Han-Fu CHEN is a Professor at the Key Laboratory of Systems and Control of Chinese Academy of Sciences. His research interests are mainly in stochastic systems, including system identification, adaptive control, and stochastic approximation and its applications to systems, control, and signal processing. He served as an IFAC Council Member (2002–2005), President of the Chinese Association of Automation (1993–2002), and a Permanent member of the Council of the Chinese Mathematics Society (1991–1999). He is an IEEE Fellow, IFAC Fellow, a Member of TWAS, and a Member of Chinese Academy of Sciences.
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Liu, R., Chen, HF. Distributed and recursive blind channel identification to sensor networks. Control Theory Technol. 15, 274–287 (2017). https://doi.org/10.1007/s11768-017-7086-x
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DOI: https://doi.org/10.1007/s11768-017-7086-x