Dimension-Reduced Searching Method for 3-D Target Imaging in Curvilinear SAR
三维目标曲线SAR成像的降维搜索算法

CurviLinear Synthetic Aperture Radar (CLSAR), whose aperture is formed via single curvilinear trajectory, has the capability of three-dimensional (3-D) imaging. The 3-D images obtained by using non-parametric methods, however, have little practical use because the data collected by CLSAR is sparse in 3-D frequency space. Valuable 3-D target images are obtained by parametric methods. In this paper, a new algorithm is proposed for imaging 3-D target in CLSAR. With smartly utilizing the loose coupling between the range and cross-range parameters, the new algorithm reduces the problem of high dimensional optimization into several lower dimensional optimization, estimates them in sequence, and refines them via iteration. Simulation results show the new algorithm can efficiently form the target's 3-D image via CLSAR.