A relaxation-based sequential optimizing method in CLSAR

A relaxation-based sequential optimizing (RESO) method is proposed for three-dimensional (3-D) target, feature extraction via the curvilinear synthetic aperture radar (CLSAR) system. Compared with the similarly structured algorithm RELAX, RESO obtains the scatterer´s 3-D position estimate via one-dimensional (1-D) and two-dimensional (2-D) FFTs instead of 3-D FFT employed by the former. Due to the utilization of these lower dimensional FFTs, the new algorithm is more efficient. Simulation results show that RESO and RELAX have similar performance and the estimates obtained via both of them can reach the corresponding Cramer-Rao bounds (CRB).