Sparse Sampled MIMO radar for angle-range-doppler imaging

MIMO radar can provide higher resolution, improve sensitivity, and increase parameter identifiability without considering sparse sampled. Sparse signal recovery algorithms can offer improved estimation when the scene of interest contains a limited number of targets. In this paper, we present a modified approach to sparse signal recovery. The proposed approach follows an l_q-norm constraint (for 0<;0<;1) and can provide increased sparsity via iterative minimization compared to existing approaches. Simulation results show the proposed approach provides superior performance for sparse MIMO radar imaging applications at a low computational cost.