An Improved Ambiguity Echo Separation Strategy for Multichannel SAR Based on Independent Component Analysis

High-resolution, wide-range imaging is crucial for contemporary and future remote sensing surveys. Multichannel synthetic aperture radar (SAR) is an effective tool to fulfill this requirement. Among various multichannel technologies, null steering digital beamforming is commonly used for ambiguity suppression, SAR echo separation, and interference and clutter suppression. However, existing null steering beamforming algorithms lack robustness and are sensitive to channel errors. This article proposes an improved SAR echo separation scheme based on complex independent component analysis (ICA) to address the channel error issue. Initially, the problem caused by channel error leading to the failure of the linear constraint minimum variance (LCMV) beamformer is analyzed. Then, non-Gaussian and noncircular measurements of SAR echoes from different scatterers are conducted, concluding that multichannel SAR echoes satisfy the conditions for ICA application. For an elevated multichannel wide-swath SAR, an ambiguity signal separation scheme based on ICA is proposed. Block processing or pure-decimal-constraint LCMV (PDC-LCMV) beamforming is first employed to compensate for the spatial dependence of the mixing matrix. A complex entropy-bound maximum algorithm, based on source signal independence, is then used for further signal separation. Our proposed scheme effectively addresses the channel error issue in traditional null steering beamforming schemes and efficiently separates ambiguity echoes. It leverages the statistical characteristics of the signal itself and expands the processing dimension. Capable of handling multiple errors, it is suitable for systems with limited degrees of freedom and does not require additional hardware resources. Simulation experiments verify the effectiveness of this scheme.