Abstract
Curvilinear synthetic aperture radar (CLSAR), whose aperture is formed via a curvilinear trajectory, is considered as a practical three-dimensional (3D) imaging system. 3D images obtained by using non-parametric methods, however, have little practical use because the data collected by CLSAR is sparse in 3D frequency space. Some parametric methods have been successfully applied to CLSAR for imaging, but are computationally expensive since they are iteration methods. A non-iterative imaging (NII) algorithm is proposed. The new algorithm estimates the range parameters of all scatterers via a modern spectrum method. It then uses these range estimates and the received data to form two-dimensional (2D) data slices, from which the cross-range parameters are estimated. Once the position (range and cross-range) estimates are obtained, the radar cross section (RCS) can be calculated from the data. Simulation results show that the new algorithm can efficiently form the target's 3D image via CLSAR.
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