On Efficient Maximum Likelihood Algorithm for Clutter Suppression

Nonside-looking mode for airborne radar system is important to detect targets. However, the nonlinear distribution of clutter results traditional space-time adaptive processing (STAP) performance degradation. To address the off-grid effect, an effective STAP algorithm is proposed. We first formulate the block-Toeplitz structured clutter covariance matrix (CCM) recovery problem using the stochastic maximum likelihood (ML) criterion. Then, we employ the majorization-minimization (MM) frame to solve the non-convex ML optimization problem. Finally, extensive simulation results evidence the performance of our method.