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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Bibliographic Details
Published in:IEEE transactions on signal processing 2014-03, Vol.62 (6), p.1386-1395
Main Authors: Liu, Zhaoting, Liu, Ying, Li, Chunguang
Format: Article
Language:English
Subjects:
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
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Summary: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.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2014.2302731