三维目标曲线SAR成像的降维搜索算法

曲线合成孔径雷达(CurviLinear Synthetic Aperture Radar，CLSAR)利用雷达平台的单条曲线轨迹就可形成三维成像所需的曲线合成孔径。由于CLSAR采集的数据在三维频率空间是稀疏的，简单地采用非参数化方法所获得的图像几乎无法使用，所以有价值的目标三维像必须采用参数化方法来获得。该文提出一种新的适用于CLSAR的目标三维成像算法。该算法巧妙地利用了接收数据中距离方向与垂直距离方向参数间的弱耦合性，将高维优化问题解耦为低维优化问题，并顺序地估计出相应参数，最后采用一个迭代过程进行参数求精。仿真实验表明，新算法是一种适用于CLSAR的有效的目标三维成像算法。

Dimension-Reduced Searching Method for 3-D Target Imaging in Curvilinear SAR

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.