Distributed Sparse Recursive Least-Squares Over Networks

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 distribu...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 62; H. 6; S. 1386 - 1395
Hauptverfasser:	Liu, Zhaoting, Liu, Ying, Li, Chunguang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.03.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	adaptive signal processing Algorithm design and analysis Algorithms Applied sciences Computational complexity Detection, estimation, filtering, equalization, prediction distributed estimation Exact sciences and technology expectation-maximization algorithm Information, signal and communications theory Least squares approximations Least squares method Mathematical analysis Mathematical models Maximum likelihood estimation Networks Operators Recursive recursive least square Signal and communications theory Signal processing algorithms Signal, noise Sparsity Telecommunications and information theory Transaction processing Vectors Wireless sensor network Performance evaluation Threshold detection Wireless telecommunication Adaptive signal processing sparsity Adaptive method Implementation Remote sensing recursive least square Distributed processing Least squares method Signal detection Information exchange distributed estimation expectation-maximization algorithm Recursive algorithm Computational complexity Wireless sensor network Recursive method Signal processing Wireless network Numerical simulation Maximum likelihood Sensor array EM algorithm
ISSN:	1053-587X, 1941-0476
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Zusammenfassung:	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 l0- and l1-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.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2014.2302731