Adaptive weight matrix design and parameter estimation via sparse modeling for MIMO radar

Although sparse representation and sparse recovery algorithms for colocated multiple-input multiple-output (MIMO) radar have received much attention, incoherence of the sensing matrix received few discussions. In this paper, we propose adaptive weight matrix design and parameter estimation via sparse modeling to improve the recovery performance for MIMO radar. First, a sparse framework is formulated for the MIMO array with decoupled transmit weight matrix and steering matrix. Next, a two stage method is proposed to optimize the two matrices to improve the DOA estimation performance. Finally, a sparse recovery approach based on lq (0 < q ≤ 1) norm optimization is developed to estimate the unknown target parameters. Furthermore, the algorithm can be iteratively implemented with the obtained closed-form solution for the optimization problem. The angle-amplitude estimation performance is examined by analyzing the Cramér-Rao lower bound (CRLB). Numerical results demonstrate that significant performance improvement has been achieved by the proposed sensing matrix optimization and adaptive weight matrix design approach.