基于压缩感知的曲线SAR孔径优化和目标三维特征提取

基于压缩感知理论研究了曲线合成孔径雷达的曲线孔径优化和目标三维特征提取。在建立曲线合成孔径雷达回波信号稀疏表示模型的基础上，基于压缩感知采样矩阵设计的不相关原则，给出了曲线孔径优化设计的评价准则，并利用基于全局优化的基追踪方法实现了目标三维特征提取。仿真结果验证了孔径优化评价准则的正确性和基追踪方法在目标特征提取处理中的有效性。

Aperture Optimizing and Target Feature Extraction of Curvilinear SAR Based on Compress Sensing

The Compress Sensing theory is applied to curvilinear aperture optimizing and 3-dimentional target feature extraction of Curvilinear Synthetic Aperture Radar. First, the target sparse representation model is built. Then, based on the incoherence property between sparse matrix and sampling matrix of the compress sensing theory, a way to direct curvilinear aperture optimizing is found. Moreover, by employing the Basis Pursuit method based on overall optimizing, 3-dimentional target feature extraction is realized. The simulate results prove the conclusion from the aperture optimization strategies with the incoherence property, as well as the efficiency of the BP method in target feature extraction.