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

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 STAP in heterogeneous clutter. 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 expanded training samples set, that are composed of 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 to the cell under test are assigned higher weights in the estimation of the clutter covariance matrix. The proposed method overcomes the problems of training samples’ heterogeneity and insufficiency in the estimation of the 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.