An Airborne Radar Clutter Suppression Algorithm Based on Negative Log-Likelihood Minimization

Space time adaptive processing (STAP) plays an important role in the field of airborne radar clutter suppression. However, due to the inhomogeneity of the clutter environment, the radar system is unable to obtain enough training samples, which will lead to a significant reduction in the clutter suppression performance of STAP. To address this issue, an airborne radar clutter suppression algorithm based on negative log-likelihood minimization is proposed in this work. First, we use the negative log-likelihood function minimization criterion to give the cost function for estimating the clutter plus noise covariance matrix (CNCM), which is a nonconvex optimization problem. Second, to estimate the CNCM, we use the majorization-minimization (MM) algorithm and alternating optimization to iteratively solve the above nonconvex problem. Finally, we utilize the estimated CNCM to construct a space-time filter to suppress the ground clutter. Simulation results show that the proposed algorithm performs well in terms of both clutter suppression and convergence speed in the case of insufficient training samples.