Conformal-array STAP using sparse representation

Space-time adaptive processing (STAP) is a well-suited technique to detect the slow-moving target in the presence of a clutter-spreading environment. When considering the airborne radar deployed with conformal array (CFA), the training data behaves range-dependent, which results in poor detection performance with traditional STAP methods. In this paper, we combine the registration-based compensation with the technique of sparse representation (RBC-SR) to generate nearly stationary training data. The simulation shows that: since the spectral response are obtained with full-snapshot using sparse representation, RBC-SR can provide more accurate clutter spectral estimation at each range cell so that the compensated training data behaves more stationary and better signal-clutter-ratio (SCR) improvement is achieved.