Distributed Sparse Recursive Least-Squares Over Networks
IEEE Transactions on Signal Processing(2014)
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 $\ell_0$ - and $\ell_1$ -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.
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关键词
sparse vectors,distributed estimation,sparse estimation problem,distributed algorithms,expectation-maximisation algorithm,recursive least square,signal processing,expectation-maximization algorithm,distributed algorithm,wireless sensor network,adaptive signal processing,sparse recursive least square algorithm,least squares approximations,maximum likelihood algorithm,sparsity,recursive estimation,thresholding operator,in-network distributed estimation,vectors,computational complexity,maximum likelihood estimation,expectation maximization algorithm,algorithm design and analysis
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