Colored-Loading Factor Optimization for Airborne KA-STAP Radar
Yuanyi Xiong, Wenchong Xie, Hu Li, Xunzhang Gao
Abstract
Colored-loading factor optimization is one of the key technologies in the knowledge-aided space-time adaptive processing (KA-STAP) field. In this article, a novel colored-loading factor optimization method based on prewhitening (PW) performance evaluation in the direct data domain [direct PW (DPW)] is proposed. As for the proposed method, first, prior knowledge is used for channel pair processing to realize the effective estimation of the space-time covariance matrix tapers. Second, the possible target signal is eliminated by orthogonal projection, and the echo samples of different range cells to be detected are obtained by space-time subaperture smoothing. Finally, the optimization function is constructed based on the PW in the direct data domain, and the optimal colored-loading factor is obtained by searching. On the one hand, the DPW method improves the accuracy of prior information by using the estimated space-time covariance matrix tapers; on the other hand, the colored-loading factors of different range cells are obtained. Thus, it effectively solves the problem that the priori covariance matrix may have differences in range cells. Computer simulation results verify the effectiveness of the proposed method.