Multichannel Structured Low-Rank and Sparse Method for 3-D Interferometric Radar Imaging

The interferometric inverse synthetic aperture radar (InISAR) is an advanced three-dimensional (3-D) imaging sensor that enhances the interpretation and recognition of moving target, such as satellite and aircraft. In the practical multi-functional radar, it often suffers from the data sparse sampling, such as frequency band and sparse aperture (SA), which greatly increases the difficulty of InISAR imaging. In this paper, a multichannel structured low-rank Hankel and sparse matrix recovery (MC-SLRHSMR) algorithm is proposed for high-resolution InISAR imaging from compressively sampled data, which can effectively maintain a high coherence between multichannel data with enhanced performance of 3-D target geometry reconstruction. In the scheme, a Hankel matrix formulation is utilized to characterize the intrinsic low-rank property of multichannel data, exploiting the data structure in both temporal and spatial dimensions. Meanwhile, a reweighted approach of joint sparsity constraint on multichannel images is incorporated to construct a unified imaging diagram for the enhanced performance. Then, the Hankel matrix factorization is used to avoid the complicated computation of singular value decomposition (SVD) and the imaging problem is solved in linearized minimization using an alternating direction method of multipliers (ADMM) approach. Next, the 3-D target geometry is optimized with outlier removing and error reduction while the squinted-angle InISAR is also addressed with modified signal model and accurate coordinate compensation method. Finally, experiments based on both simulated and measured data are conducted to validate the effectiveness of the proposed algorithm, demonstrating its superiority over traditional sparse imaging methods such as compressive sensing (CS).