An Improved Sparse Recovery Direct Data Domain STAP Method

‍ ‍Sparse Recovery Space-Time Adaptive Processing （SR-STAP） can estimate the Clutter Covariance Matrix （CCM） by using a few of training snapshot accurately. However， the complex clutter environment against the detection of moving targets. In order to improve the detection performance， this paper proposes an improved sparse recovery Direct Data Domain STAP method. Firstly， the global space-time steering vector dictionary of the snapshot data under detection is constructed， and the sparse vector of snapshot are obtained by the sparse recovery process. Then， the clutter and target elements in sparse vector are picked out by prior knowledge of array. At the end， the clutter power spectrum and the target power spectrum can be obtained， and the target is detected by filtering. The experimental results indicate that the target and the clutter can be divided by the method with single snapshot. Moreover， it can not only avoid non-uniformity of clutter distribution， but also improve the detection performance of moving target.