逆合成孔径成像激光雷达低信噪比稀疏多孔径成像方法研究

受目标非合作特性的影响，逆合成孔径成像激光雷达(ISAIL)回波存在缺失；同时受大气衰减和自然背景光等因素的影响，ISAIL回波信号信噪比较低，因此，常规的稀疏多孔径成像方法不再适用。针对上述问题，该文提出了一种结合压缩感知(CS)和权矩阵的稀疏多孔径成像方法。首先，通过基于CS的稀疏多孔径成像方法对原始数据处理，得到目标像的支撑域；然后，据此建立权矩阵，优化采用CS重构时的代价函数，对稀疏多孔径ISAIL原始数据进行成像处理，利用不完整的回波信号获得高分辨目标像。此算法具有较好的抗噪能力。采用室内ISAIL系统实测数据验证了算法的有效性。

Study on Sparse Aperture of Inverse Synthetic Aperture Imaging Ladar with Low SNR

The traditional sparse aperture methods are not suitable for signal processing of Inverse Synthetic Aperture Imaging Ladar (ISAIL) for the following reasons. One is the lack of echoes caused by the noncooperation characteristic of the target, the other is the low Signal-to-Noise Ratio (SNR) caused by the influence of atmosphere attenuation and natural background light. For this reason, a novel sparse aperture imaging method is proposed in this paper which combines the Compressed Sensing (CS) with the weighted matrix. Through the preprocessing of CS based imaging, the supporting field of the target can be obtained, with which the weighted matrix can be constructed. Then the cost function is optimized using the weighted matrix. Finally, with this new cost function, the high resolution image can be achieved using the incomplete original echoes from the sparse aperture ISAIL. This new method is robust in front of strong noise. The feasibility and effectiveness of the method are validated by the measured data of the indoor ISAIL system.