Blind Channel and Source Estimation in Networked Systems

Chengpu Yu, Lihua Xie, Yeng Chai Soh

Abstract: In this paper, we study the blind channel and source estimation in sensor networks, where the channels are modeled by FIR filters and the source signal is deterministic. Distributed estimation algorithms for networked systems under noise-free and noisy measurements are developed, which blindly identify the multiple channels, followed by the source signal estimation. The key to the proposed algorithms lies in the adaptation of the blind system identification technique for the distributed channel estimation. In the presence of measurement noises, conventional blind identification methods cannot be straightforwardly realized in distributed environments. Instead, two stable distributed algorithms are introduced, which can avoid trivial solutions for the blind identification problem. Convergence properties of the proposed algorithms are provided, and simulation examples are given to show the performances of the proposed algorithms.