Robust waveform design for MIMO-STAP with imperfect clutter prior knowledge

In this paper, we address the problem of robust waveform optimization with imperfect clutter prior knowledge to improve the worst-case detection performance of multi-input multi-output (MIMO) space-time adaptive processing (STAP) in the presence of colored Gaussian disturbance. An iterative algorithm is proposed to optimize the waveform covariance matrix (WCM) for maximizing the worst-case output signal-interference-noise-ratio (SINR) over the convex uncertainty set such that the worst-case detection performance of MIMO-STAP can be maximized. By exploiting the diagonal loading (DL) method, each iteration step in the proposed algorithm can be reformulated as a semidefinite programming (SDP) problem, which can be solved very efficiently. Numerical examples show that the worst-case output SINR of MIMO-STAP can be improved considerably by the proposed method compared to that of uncorrelated waveforms.