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

In this paper, the robust waveform optimization problem with imperfect clutter prior knowledge is addressed for improving the detection performance of MIMO-OFDM radar based STAP in the complex environment. Robust waveform optimization is needed due to that fact that MEMO radar waveform design is often sensitive to estimation errors in the initial parameter estimates (i.e., some prior information on the target of interest and scenario), which attempts to systematically alleviate this sensitivity by explicitly incorporating a parameter uncertainty model into the optimization problem. An iterative algorithm is proposed to optimize the waveform covariance matrix (WCM) for maximizing the worst-case output signal interference-noise-ratio (SENR) over the convex uncertainty set such that the worst-case detection performance of MIMO-OFDM-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 results show that the worst-case performance can be improved considerably by the proposed method compared to uncorrelated waveforms.