Robust and fast iterative sparse recovery method for space-time adaptive processing  

作　　者：Xiaopeng YANG Yuze SUN Tao ZENG Teng LONG 

机构地区：[1]Beijing Key Laboratory of Embedded Real-time Information Processing Technology,School of Information and Electronics, Beijing Institute of Technology

出　　处：《Science China(Information Sciences)》2016年第6期195-207,共13页中国科学（信息科学）（英文版）

基　　金：supported by 111 Project of China (Grant No. B14010);National Natural Science Foundation of China (Grant Nos. 61225005, 61120106004)

摘　　要：Conventional space-time adaptive processing（STAP） requires large numbers of independent and identically distributed（i.i.d） training samples to ensure the performance of clutter suppression, which is hard to be achieved in practical complex nonhomogeneous environment. In order to improve the performance of clutter suppression with small training sample support, a robust and fast iterative sparse recovery method for STAP is proposed in this paper. In the proposed method, the sparse recovery of clutter spatial-temporal spectrum and the calibration of space-time overcomplete dictionary are achieved iteratively. Firstly, the robust solution of sparse recovery is derived by regularized processing, which can be calculated recursively based on the block Hermitian matrix property, afterwards the mismatch of space-time overcomplete dictionary is calibrated by minimizing the cost function. The proposed method can not only alleviate the effect of noise and dictionary mismatch, but also reduce the computational cost caused by direct matrix inversion. Finally, the proposed method is verified based on the simulated and the actual airborne phased array radar data, which shows that the proposed method is suitable for practical complex nonhomogeneous environment and provides better performance compared with conventional STAP methods.Conventional space-time adaptive processing（STAP） requires large numbers of independent and identically distributed（i.i.d） training samples to ensure the performance of clutter suppression, which is hard to be achieved in practical complex nonhomogeneous environment. In order to improve the performance of clutter suppression with small training sample support, a robust and fast iterative sparse recovery method for STAP is proposed in this paper. In the proposed method, the sparse recovery of clutter spatial-temporal spectrum and the calibration of space-time overcomplete dictionary are achieved iteratively. Firstly, the robust solution of sparse recovery is derived by regularized processing, which can be calculated recursively based on the block Hermitian matrix property, afterwards the mismatch of space-time overcomplete dictionary is calibrated by minimizing the cost function. The proposed method can not only alleviate the effect of noise and dictionary mismatch, but also reduce the computational cost caused by direct matrix inversion. Finally, the proposed method is verified based on the simulated and the actual airborne phased array radar data, which shows that the proposed method is suitable for practical complex nonhomogeneous environment and provides better performance compared with conventional STAP methods.

关 键 词：space-time adaptive processing（STAP） sparse recovery robust iteration computational complexity 

分 类 号：TN958.92[电子电信—信号与信息处理]