Registration-based Compensation using Sparse Representation in Conformal-array STAP

Space-time adaptive processing (STAP) is a well-suited technique to detect slow-moving targets in the presence of a clutter-spreading environment. When considering the STAP system deployed with conformal radar array (CFA), the training data is range-dependent, which results in poor detection performance of traditional statistical-based algorithms. Current registration-based compensation (RBC) is implemented based on sub-snapshot spectrum using temporal smoothing. In this case, the estimation accuracy of the configuration parameters and the clutter power distribution is limited. In this paper, we introduce the technique of sparse representation into the spectral estimation and propose a new compensation method, called RBC with sparse representation (SR-RBC). This method first converts the clutter spectral estimation into an ill-posed problem with the constraint of sparsity. Then the technique of sparse representation like iterative reweighted least squares (IRLS) is utilized to solve this problem. Based on this, the transform matrix is designed so that the processed training data behaves nearly stationary with the test cell. Since the configuration parameters as well as the clutter spectral response are obtained with full-snapshot using sparse representation, SR-RBC provides more accurate clutter spectral estimation and the transformed training data is more stationary so that better signal-clutter-ratio (SCR) improvement is expected.