A Noniterative Online Bayesian Algorithm for the Recovery of Temporally Correlated Sparse Vectors

Joseph, Geethu and Murthy, Chandra R (2017) A Noniterative Online Bayesian Algorithm for the Recovery of Temporally Correlated Sparse Vectors. In: IEEE TRANSACTIONS ON SIGNAL PROCESSING, 65 (20). pp. 5510-5525.

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Abstract

In this paper, we address the problem of online (sequential) recovery of temporally correlated sparse vectors sharing a common support, from noisy underdetermined linear measurements. The temporal correlation of the sparse vectors is modeled using a first-order autoregressive process. The online algorithm is formulated using the sparse Bayesian learning framework and is implemented using a sequential expectation-maximization procedure. Our algorithm is noniterative in nature, and requires less computational and memory resources compared to offline processing. We analyze the convergence of the algorithm in the case when the sparse vectors are uncorrelated, using tools from stochastic approximation theory. We show that the sequence of the covariance estimates converge either to the global minimum of the offline equivalent cost function or to the all zero vector, regardless of the sparsity level of the signal. Through numerical results, we demonstrate the efficacy of the proposed online algorithm and compare it with its offline counterpart as well as with existing online sparse vector recovery algorithms. We also illustrate the performance of the algorithm in the context of sparse orthogonal frequency division multiplexing channel estimation.

Item Type:	Journal Article
Publication:	IEEE TRANSACTIONS ON SIGNAL PROCESSING
Additional Information:	Copy right for this article belongs to the IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering Division of Electrical Sciences > Electrical Communication Engineering > Electrical Communication Engineering - Technical Reports
Date Deposited:	23 Sep 2017 05:13
Last Modified:	23 Sep 2017 05:13
URI:	http://eprints.iisc.ac.in/id/eprint/57846

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