基于改进SVA和压缩感知的SAL旁瓣抑制算法

针对机载合成孔径激光雷达实测数据成像中旁瓣较高的问题，提出一种新旁瓣抑制算法。压缩感知理论表明，稀疏信号恢复重构过程的同时，信号旁瓣会被压低，但合成孔径激光雷达图像不是稀疏的。针对这一问题，提出了一种基于改进SVA（Spatially Variant Apodization）和压缩感知重构SAL图像的旁瓣抑制算法。首先，利用改进SVA算法将SAL图像变稀疏，然后再利用压缩感知算法对稀疏图像进行恢复。分别对SAL仿真数据和实际高旁瓣SAL复图像进行抑制旁瓣处理。仿真结果表明:该算法能够在保证主瓣不被展宽的前提下有效抑制SAL旁瓣。

Sidelobe-suppression algorithm of SAL data with modified SVA and compressive sensing

A new sidelobe-suppression algorithm was proposed for the synthetic aperture ladar(SAL) with high sidelobe data. The theory of compressed sensing (CS) indicates that the sidelobe of the sparse signal can be lowered while reconstructing the signal, but the image signal of SAL is not sparse. Therefore, a sidelobe suppressing algorithm based on the modified spatial variant apodization (SVA) and SAL image reconstructed by the CS was proposed to deal with the high-sidelobe problem in real-time data imaging. SAL image signal would be converted to be sparse by the modified SVA first and the sparse signal would be reconstructed by the CS. The sidelobe of the SAL simulation data and the real high-sidelobe SAL image data were all suppressed respectively. The simulation result shows that in the premise of no broadening mainlobe, the sidelobe of the SAL image signal can be effectively suppressed by this algorithm.