Distributed Sparse Signal Estimation in Sensor Networks Using H_∞-Consensus Filtering

作     者：Haiyang Yu Yisha Liu Wei Wang 

作者机构：Research Center of Information and ControlDalian University of Technology School of Information Science and TechnologyDalian Maritime University 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2014年第1卷第2期

页      面：149-154页

核心收录：

学科分类：07[理学] 080202[工学-机械电子工程] 08[工学] 070104[理学-应用数学] 0802[工学-机械工程] 081101[工学-控制理论与控制工程] 0701[理学-数学] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foundation of China  (6130512) 

主　　题：Kalman filters Sensors Estimation Mathematical model Sparse matrices Covariance matrices 

摘      要：This paper is concerned with the sparse signal recovery problem in sensor networks, and the main purpose is to design a filter for each sensor node to estimate a sparse signal sequence using the measurements distributed over the whole network. A so-called 1-regularized H_∞filter is established at first by introducing a pseudo-measurement equation, and the necessary and sufficient condition for existence of this filter is derived by means of Krein space Kalman *** embedding a high-pass consensus filter into 1-regularized H∞filter in information form, a distributed filtering algorithm is developed, which ensures that all node filters can reach a consensus on the estimates of sparse signals asymptotically and satisfy the prescribed H∞performance constraint. Finally, a numerical example is provided to demonstrate effectiveness and applicability of the proposed method.