A novel KA-STAP method based on Mahalanobis distance metric learning

The estimation accuracy of the clutter covariance matrix will be degraded when using the heterogeneous and contaminated training samples. To improve the performance of clutter suppression in heterogeneous environments, a novel knowledge aided space-time adaptive processing method is proposed in this paper, which is based on Mahalanobis distance metric learning (MML-KA STAP). Exploiting the difference of the Mahalanobis distances between the...