Robust clutter suppression in heterogeneous environments based on multi frames and similarities

Abstract

A method of robust clutter suppression with space time adaptive processing (STAP) for airborne radar in heterogeneous environments is proposed, which is based on multi frames and the similarity between the cell under test and each training sample. The proposed method deals with the problem of covariance matrix estimation for radar signal processing, and it provides a solution to overcome the performance degradation of STAP in heterogeneous environments and training samples limitation. Firstly, the method expands the set of training samples by selecting similar training frames from past frames. Secondly, initial training samples are selected from the expended training samples set, which is composed by the samples of the current frame and past frames. Thirdly, initial training samples which may be contaminated by target signal are discarded. Fourthly, the similarities between the cell under test and the remaining training samples are estimated, and training samples which are more similar with the cell under test take higher weight in the estimation of clutter covariance matrix. The accuracy of the estimated clutter character is improved significantly, and thus the performance of clutter suppression is improved. Experimental results based on measured data demonstrate the performance of the proposed method.