Multichannel SAR Moving-Target Detection Based on HPD Manifold in Heterogeneous Clutter

Heterogeneous clutter suppression is an important challenge for multichannel synthetic aperture radar (SAR) moving-target detection. The knowledge-aided space–time adaptive processing (STAP) method is widely used due to its superior clutter suppression performance. In this process, it is necessary to use independent and identically distributed (IID) training data to estimate the clutter covariance matrix (CCM) of the cell under test (CUT). However, heterogeneous environments such as clutter power fluctuations and moving-target contamination in training samples will significantly reduce the estimation accuracy of the CCM, which is not conducive to subsequent clutter suppression processing. To solve this problem, this article introduces the covariance matrix estimation method based on the geometric mean of the Hermitian positive-definite (HPD) manifold into the multichannel SAR moving-target detection. First, the airborne multichannel SAR echo is characterized on the HPD manifold, which better reveals the nonlinear geometric structure of the multichannel SAR echo data than in Euclidean space. Based on the echo characterization on the HPD manifold, the CCM estimation problem is transformed into the geometric mean estimation problem on the HPD manifold. At the same time, the generalized inner product (GIP) criterion is used in training sample selection to reduce the negative impact of contaminated samples. Finally, the CCM estimation based on geometric mean is applied to the knowledge-aided SAR-STAP, which effectively improves the clutter suppression performance. Given the large volume of multichannel SAR echo data, this article comprehensively evaluates algorithm performance and computational complexity and selects the geometric metric criterion for multichannel SAR moving-target detection. Based on this criterion, a multichannel SAR moving-target detection framework driven by the HPD manifold is proposed. The detection performance of the proposed framework is verified on simulated and measured clutter backgrounds.