Robust waveform design for MIMO-STAP to improve the worst-case detection performance

摘要

In this article, we address the problem of robust waveform optimization for improving the worst-case detection performance of multi-input multi-output (MIMO) space–time adaptive processing (STAP) in the presence of colored Gaussian disturbance. A novel diagonal loading-based method is proposed to optimize the waveform covariance matrix 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. The resultant nonlinear optimization problem is reformulated as a semidefinite programming 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.