Radar Target Detection in Heterogeneous Environment Based on HPD Manifold Clustering

Target detection, especially in heterogeneous environments with strong clutter, is a difficult problem in signal processing. The existence of strong clutter in the heterogeneous environment usually causes the estimate of the clutter covariance matrix (CCM) to deviate from the truth, resulting in a degradation in detection performance. To address this problem, a novel CCM estimate method based on heterogeneous clutter clustering is proposed to improve the performance of radar detectors. First, it uses the distribution characteristics of clutter samples on the Hermitian positive-definite (HPD) manifold to segregate the clutter into clusters and select representative samples. Second, based on the cluster centers, a covariance matrix that can enhance the discrimination between the target echo and clutter on the HPD manifold is constructed. The proposed method can effectively enhance the discrimination between target and clutter because the reconstructed covariance matrix can reflect the clutter characteristics better. The advantage of the proposed method is demonstrated by experimental results on both simulated and real data. Compared with the conventional CCM estimation method based on the Log-Euclidean (LE) mean, the proposed method can improve the detection probability by 29%.