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Abstract: The problem of recovering a sparse solution from Multiple Measurement Vectors (MMVs) is a fundamental issue in the field of signal processing. However, the performance of existing recovery algorithms is far from satisfactory in terms of maximum recoverable sparsity level and minimum number of measurements required. In this paper, we present a high-performance recovery method which mainly has two parts: a versatile recovery framework named RPMB and a high-performance algorithm for it. Specifically, the RPMB framework improves the recovery performance by randomly projecting MMV onto a subspace with lower and tunable dimension in an iterative procedure. RPMB provides a generalized framework in which the popular ReMBo (Reduce MMV and Boost) algorithm can be regarded as a special case. Furthermore, an effective algorithm that can be embedded in RPMB is also proposed based on a new support identification strategy. Numerical experiments demonstrate that the proposed method outperforms state-of-the-art methods in terms of recovery performance. [Copyright &y& Elsevier]

The multiple measurement vector (MMV) problem addresses the identification of unknown input vectors that share common sparse support. Even though MMV problems have been traditionally addressed within the context of sensor array signal processing, the recent trend is to apply compressive sensing (CS) due to its capability to estimate sparse support even with an insufficient number of snapshots, in which case classical array signal processing fails. However, CS guarantees the accurate recovery in a probabilistic manner, which often shows inferior performance in the regime where the traditional array signal processing approaches succeed. The apparent dichotomy between the probabilistic CS and deterministic sensor array signal processing has not been fully understood. The main contribution of the present article is a unified approach that revisits the link between CS and array signal processing first unveiled in the mid 1990s by Feng and Bresler. The new algorithm, which we call compressive MUSIC, identifies the parts of support using CS, after which the remaining supports are estimated using a novel generalized MUSIC criterion. Using a large system MMV model, we show that our compressive MUSIC requires a smaller number of sensor elements for accurate support recovery than the existing CS methods and that it can approach the optimal l_0-bound with finite number of snapshots even in cases where the signals are linearly dependent. [ABSTRACT FROM PUBLISHER]