Adaptively iterative weighting covariance matrix estimation for airborne radar clutter suppression

In partially homogeneous clutter environments, space-time adaptive processing (STAP) often suffers from performance loss due to the variation of clutter power. In this paper, the covariance matrix (CM) of the cell under test (CUT) required by STAP is derived as a weighting summation of the CM of training samples, and an iterative method is developed to calculate the weighting coefficients, which are shown to be relevant to both the CUT and the training samples. The resultant CM estimation has an interesting constant false alarm rate (CFAR) property with respect to clutter power fluctuation, and thus results in a significant performance improvement for STAP. Experiments on real data and simulated data demonstrate the effectiveness of the proposed method.