Abstract: 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. Due to the fact that maximization of the output signal-interference-noise-ratio (SINR) is equivalent to maximizing the detection performance of MIMO-STAP in the case of Gaussian disturbance (including clutter, jamming, and thermal noise), based on the model of the estimation error of the clutter covariance matrix built in this paper, with the total transmitted power and parameter uncertainty convex set constraints, the robust waveform optimization problem can be derived. To tackle the resultant complicated and nonlinear issue, an iterative algorithm is proposed to optimize the waveform covariance matrix (WCM) for maximizing the worst-case output 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 verify the effectiveness of the proposed method, as compared to the non-robust method and uncorrelated waveforms.