A Novel Clutter Covariance Matrix Estimation Method Based on Spacetime Local Sliding Window

The clutter suppression performance of airborne radar depends on the estimation accuracy of clutter covariance matrix. The Brennan criterion requires that the number of training samples should be greater than twice the system degrees of freedom in space time adaptive processing. In this paper, a clutter covariance matrix estimation method based on space-time local sliding window is proposed. For multi-beam locations data, joint domain localized method is used to obtain local clutter data in beam-Doppler domain. Then, the local data is interpolated to improve the signal dimension by adopting bilinear interpolation, and the training samples are obtained by the space-time local sliding window of the interpolated data along the clutter ridge. Different from traditional methods, the proposed method can make full use of the clutter information of a single range ring in beam-Doppler domain and the problem of insufficient training samples of range domain can be ameliorated. The simulation results show the effectiveness of the proposed method.