基于NCS算子的大斜视SAR压缩感知成像方法

该文针对大斜视合成孔径雷达(Synthetic Aperture Radar, SAR)成像进行研究,提出了一种基于非线性频调变标(Non-linear Chirp Scaling, NCS)算子的大斜视SAR压缩感知成像方法。首先在详细分析大斜视SAR回波信号模型的基础上,给出了一种基于全采样数据的NCS成像算法,该算法有效完成了回波数据的走动补偿与解耦合处理,实现了准确成像。其次针对降采样的大斜视SAR回波数据成像问题,提出将上述成像算法构造成NCS算子并基于该算子建立压缩感知重构模型,通过对模型的优化求解直接获得最终的成像结果。该方法对于稀疏性成像场景能够有效降低回波数据采样率实现高质量成像,对于非稀疏成像场景在满采样条件下能够提高成像质量。最后的点目标和面目标的仿真实验验证了该文所提方法的有效性和可行性。 

Compressed Sensing Imaging Algorithm for High-squint SAR Based on NCS Operator

A novel compressed sensing imaging algorithm for high-squint Synthetic Aperture Radar (SAR) based on a Nonlinear Chirp-Scaling (NCS) operator is proposed. First, the echo signal of high-squint SAR is analyzed, and a novel imaging method based on the Nyquist-sampled echo signal is proposed. With the proposed method, the range migration is corrected and the coupling problem in the range and azimuth directions is solved. Then, to solve the problem of high-squint SAR imaging using undersampled echo signals, the NCS operator and compressed sensing algorithm based on this operator are constructed. Imaging results are obtained by solving an optimization problem. The proposed method can recover a sparse scene using undersampled echo data. Furthermore, it can recover a nonsparse scene using fully sampled data. Finally, simulations show the effectiveness of the proposed method.