Fast inverse covariance matrix computation based on element-order recursive method for space-time adaptive processing

Abstract

Because of large computational complexity in the inverse space-time covariance matrix computation, the conventional space-time adaptive processing (STAP) is unsuitable for practical implementation. According to the block Hermitian matrix property of covariance matrix, a new element-order recursive method is proposed in this paper to calculate the inverse space-time covariance matrix for STAP weight vector. In the proposed method, the inverse space-time covariance matrix of first element-order is initially calculated recursively based on block Hermitian matrix property, and then the inverse space-time covariance matrix of high element-order is correspondingly deduced recursively based on obtained inverse covariance matrix of previous element-order. Finally, STAP weight vector is calculated based on the final inverse covariance matrix. Afterwards, a modified reduced-dimension STAP method is derived by combining the proposed method with the m-Doppler Transformation (mDT-SAP) STAP approach. Based on the simulated and the actual airborne phased array radar data, the proposed method verified that the computational complexity is much smaller than conventional STAP methods. The proposed element-order recursive method for STAP is applicable for practical airborne phased radar system.