Performance guarantees for distributed MIMO radar based on sparse sensing

Sparse sensing-based distributed MIMO radars exploit the sparsity of the targets in the discretized target space to achieve the good target estimation performance of MIMO radars but with fewer measurements. Based on sparse sensing, the problem of target estimation is formulated as a sparse signal recovery problem, where the signal to be recovered is block sparse, or equivalently, the sensing matrix is block-diagonal and the signal to be recovered consists of equal size blocks that have the same sparsity profile. This paper develops the theoretical requirements and performance guarantees for the application of sparse recovery techniques to this problem. The obtained theoretical results confirm that exploiting the block sparsity of the target in the target space can reduce the number of measurements needed for target estimation, or can result in improved target estimation for the same number of samples.