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Online Recovery of Temporally Correlated Sparse Signals Using Multiple Measurement Vectors

Joseph, Geethu and Murthy, Chandra R and Prasad, Ranjitha and Rao, Bhaskar D (2015) Online Recovery of Temporally Correlated Sparse Signals Using Multiple Measurement Vectors. In: IEEE Global Communications Conference (GLOBECOM), DEC 06-10, 2015, San Diego, CA.

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Official URL: http://dx.doi.org/10.1109/GLOCOM.2015.7417777

Abstract

This work addresses the problem of sequential recovery of temporally correlated sparse vectors with common support from noisy under-determined linear measurements. The Kalman sparse Bayesian learning (SBL) algorithm 1] is an efficient tool for solving the problem when the temporal correlation is modeled using a first order autoregressive model. However, this method processes the input data in a batch mode, which results in high latency. We propose two online SBL algorithms which operate on the observations in a serial fashion. They are sequential expectation-maximization (EM) schemes, implemented using fixed lag smoothing and sawtooth lag smoothing. The online algorithms require significantly lower computational and memory resources compared to their offline counterparts. Also, estimates of the sparse vectors become available after a fixed delay from the time observations arrive. Using Monte Carlo simulations, we illustrate that the mean square error and support recovery performance of the proposed algorithms is very close to the offline Kalman SBL algorithm.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Depositing User: Id for Latest eprints
Date Deposited: 07 Dec 2016 05:27
Last Modified: 07 Dec 2016 05:27
URI: http://eprints.iisc.ac.in/id/eprint/55503

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