Compressive MUSIC: A Missing Link Between Compressive Sensing and Array Signal Processing

Abstract

The multiple measurement vector (MMV) problem addresses the identification of unknown input vectors that share common sparse support. Even though MMV problems had 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 {\em probabilistic} CS and {\em deterministic} sensor array signal processing have not been fully understood. The main contribution of the present article is a unified approach that unveils a {missing link} between CS and array signal processing. The new algorithm, which we call {\em 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 can approach the optimal $l_0$-bound with finite number of snapshots.

33 pages, 7 figures, Parts of this work were presented on 14/04/2010 at the SIAM Conference on Imaging Science, Chicago, USA, with the title "Multiple measurement vector problem with subspace-based algorithm"

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Cited by in corpus (3)

• Belief propagation for joint sparse recovery
• Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices
• Compressive MUSIC with optimized partial support for joint sparse recovery