Training sample selection for space–time adaptive processing based on multi-frames

As training samples are not identically distributed with cell under test (CUT) in heterogeneous environments, the performance of space–time adaptive processing (STAP) to suppress clutter degrades. To improve the performance of STAP, this study proposes a novel training sample selection method for STAP based on multi-frames. The key feature of the new method is to overcome the deficit of independent and identically distributed (IID) training samples in heterogeneous environments. First, multi-frames are selected according to similarity from continuous frames; second, training samples which are IID to the CUT from current frame are selected; finally, combine the training samples of the current frame and same range samples in reference frame into final training sample set. The proposed method is applied to real-radar data, and experimental results demonstrate the effectiveness of the proposed method.