Research On Robust Power Spectrum Sparse Recovery Space-time Adaptive Processing Method

Space-time adaptive processing(STAP)is considered as a key technique for clutter suppression and ground moving target indication(MTI)in airborne radar.However,it suffers from the limitation of independent and identically distributed(IID)training snapshots which is used for training space-time filter,in the complex nonhomogeneous environment.With the development of compressing sensing(CS)theory,sparsity-aware STAP was proposed and then rapidly became a hot topic.Compared with statistical-STAP algorithms,it has a great advantage for clutter suppression and MTI by just utilizing only a few IID snapshots in the nonhomogeneous environment.However,it relies on the accuracy of the sparse model.Some non-ideal factors(such as array errors,intrinsic clutter motion,and mismatch of space-time steering dictionary)in the real system and environment,hinders sparsity-aware STAP from its practical application.In this paper,we focus on designing robust algorithms considering array gain/phase errors,as follows:1)The sparse signal model is established in presence of array gain/phase errors.Theoretical analysis is done to show the impact of the array errors on sparsity-aware STAP.And simulation results show that the array errors have a great negative influence on sparsity-aware STAP.2)We discuss the sparsity-aware STAP algorithm based on conventional array gain/phase errors calibration,and then propose a robust sparsity-aware STAP algorithms,namely,sparsity-aware STAP algorithm using alternating iteration based on orthogonal matching pursuit(OMP)and least square(LS)algorithm.It obtains the joint estimations of the clutter spatial-temporal spectrum and the array gain/phase errors by utilizing OMP and LS.The experiments show that it performs robustly.3)In order to reduce the computational complexity of proposed sparsity-aware STAP using alternating iteration based on OMP and LS,we propose the robust and fast sparsity-aware STAP algorithm.It utilizes prior knowledge to reduce the dimensions of space-time steering dictionary,and to shrink the search space of sparse recovery.The experiments show that itoutperforms the sparsity-aware STAP using alternating iteration based on OMP and LS.The proposed robust sparsity-aware STAP considering array gain/phase errors,has a great significance on sparsity-aware STAP to overcome the impact of array gain/phase errors in practice.