文献类型：期刊文献

英文题名：Distributed Sparse Recursive Least-Squares Over Networks

作者：Liu, Zhaoting[1,2];Liu, Ying[2];Li, Chunguang[2]

机构：[1]Shaoxing Univ, Dept Elect Engn, Shaoxing 312000, Peoples R China;[2]Zhejiang Univ, Dept Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China

年份：2014

卷号：62

期号：6

起止页码：1386

外文期刊名：IEEE TRANSACTIONS ON SIGNAL PROCESSING

收录：SCI-EXPANDED(收录号：WOS:000333025000005)、、EI(收录号：20141217485934)、Scopus(收录号：2-s2.0-84900656099)、WOS

基金：The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Tereq Al-Naffouri. This work was supported by the National Natural Science Foundation of China (Grant No. 61171153 and 61101045), the Foundation for the Author of National Excellent Doctoral Dissertation of PR China, the Scientific Research Foundation for the Returned Overseas Chinese Scholars, the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR12F01001), the National Program for Special Support of Eminent Professionals, and the Open Research Grants of the Information Processing and Automation Technology Prior Discipline of Zhejiang Province.

语种：英文

外文关键词：Wireless sensor network; distributed estimation; sparsity; adaptive signal processing; recursive least square; expectation-maximization algorithm

外文摘要：Distributed estimation over networks has received much attention in recent years due to its broad applicability. Many signals in nature present high level of sparsity, which contain only a few large coefficients among many negligible ones. In this paper, we address the problem of in-network distributed estimation for sparse vectors, and develop several distributed sparse recursive least-squares (RLS) algorithms. The proposed algorithms are based on the maximum likelihood framework, and the expectation-maximization algorithm, with the aid of thresholding operators, is used to numerically solve the sparse estimation problem. To improve the estimation performance, the thresholding operators related to l(0)- and l(1)-norms with real-time self-adjustable thresholds are derived. With these thresholding operators, we can exploit the underlying sparsity to implement the distributed estimation with low computational complexity and information exchange amount among neighbors. The sparsity- promoting intensity is also adaptively adjusted so that a good performance of the sparse solution can be achieved. Both theoretical analysis and numerical simulations are presented to show the effectiveness of the proposed algorithms.