基于压缩感知的多角度合成孔径雷达成像

Abstract

Multi-aspect synthetic aperture radar (SAR) imaging is an important technique for synthesizing multi-aspect SAR data or information. Achieving multi-aspect SAR imagery is invaluable for resolution improvement, radar object visualization, classification and automatic object recognition (ATR). However, achieving multi-aspect SAR imaging is challenging because of the diversity of space sampling positions and the transmission of signal waveforms. In this study, a new method, based on the model of a multi-aspect SAR data collection, is proposed to achieve multi-aspect SAR imaging using compressive sensing and sparse reconstruction theory. First, the factors which dominate the performance of the measurement matrix were studied. It was found that the distribution of space sampling and the carrier frequency of the transmission signal were the key factors dominating the mutual coherence (MC) of the measurement matrix. The object sampling space was also a key factor in determining the robustness and resolution of multi-aspect SAR imaging. Second, the measurement matrix for the multi-aspect SAR was constructed, depending on previous analysis, by careful selection of the parameters and sampling of the object. Third, the stage-wise orthogonal matching pursuit (StOMP) algorithm was applied to achieve multi-aspect SAR imaging. This algorithm was effective in solving high-dimensional ill-posed equations. Extensive experiments validated the feasibility and stability of the proposed algorithm.