Ground Moving Target Detection With Adaptive Data Reconstruction and Improved Pseudo-Skeleton Decomposition

Ground moving target detection is one of the foremost tasks for multichannel synthetic aperture radar (SAR) system. The traditional robust principal component analysis (RPCA) method is capable of separating low-rank and sparse components from mixed echo signals, and it has been widely applied in SAR ground moving target indication (GMTI). However, it suffers from sensitivity to channel mismatch, high computational complexity, and excessively high false alarm rates. To address these issues, a novel method that combines adaptive multichannel data reconstruction (DR) with improved pseudo-skeleton decomposition (IPSD) is proposed. First, the iterative weighted approach is presented to precisely reconstruct the multichannel data vector with the joint-pixel model. After that, IPSD is presented to achieve the moving target detection, in which the row and column index sets are selected using the generalized inner product (GIP) and the amplitude histogram distribution criterion. Compared to the existing algorithms, the proposed algorithm effectively addresses the challenge of improving local region coherence in multichannel image sequences. In addition, compared to previous RPCA methods, the proposed algorithm significantly reduces false alarm rates in strong clutter backgrounds while achieving higher efficiency. Simulation results and real SAR data experiments validate the effectiveness of the proposed algorithm.