Sample-Efficient Robust MMV Recovery Algorithm

Recovering multiple measurement vectors (MMVs) with common sparse support from compressed measurements is an important problem in many applications. Theoretical results and practical algorithms that use rank properties of the MMVs have been shown to require fewer measurements. These methods use the same number of measurements for all vectors or channels, which may not be efficient. The rank-aware algorithms fail in the presence of noise as the rank property is lost. We propose an alternative strategy in which only a few channels are used for support recovery. These channels require larger measurements compared to the rest but lead to a reduction in the total number of measurements. We propose a robust rank-aware algorithm to tackle noisy scenarios and show that it results in lower errors in the estimation of sparse vectors compared to the existing approaches. The algorithm also allows trade off between the measurements and channels, which helps design flexible systems.