Super-resolution range-Doppler imaging

Abstract

The general observation model for range-Doppler imaging is established from the point of view of multiple scatter-point localisation, and the optimum imaging procedure based on the maximum likelihood principle is given. Pursuing simplified procedures, the authors present three super-resolution range-Doppler imaging methods, including the linear prediction data extrapolation DFT (LPDEDFT), the dynamic optimisation linear least-squares (DOLLS), and the Hopfield neural network nonlinear least-squares (HNNNLS) methods. The live data of a metallised scale model B-52 aircraft mounted on a rotating platform in a microwave anechoic chamber and a flying Boeing-727 aircraft as well as the simulated data of an aircraft were processed. The imaging results indicate that, compared to the conventional Fourier method, a higher resolution for the same effective bandwidth of transmitted signals and total rotation angle of the object may be obtained by these super-resolution approaches.