Noise Subspace Decomposition Utilizing Sliding Window Rank Estimation

The received navigation signal on the ground is vulnerable to interferences,which can be suppressed by space-time processing. Multistage Weiner Filter (MWF) is used in space-time processing to avoid large matrix decomposition, but the noise subspace estimation of classic MWF is inaccurate. A method is proposed to solve the problem: first, utilizing conventional MWF to roughly estimate the noise subspace dimension; then, using a sliding window to find the boundary between noise and white noise; finally, utilizing the integrated part of MWF to deduce the optimal weight, which is used to suppress interferences. Through simulation, it is proved that the distinguish capacity of the proposed method is superior to the conventional MWF on noise subspace estimation, thus better interference suppression effect is obtained. It can be concluded that: the proposed method is robust in noise subspace estimation, which enhances the interference suppression capacity of space-time array.