This paper presents three superresolution imaging methods, including the linear prediction data extrapolation DFT (LPDEDFT), the dynamic optimization linear least squares (DOLLS), and the Hopfield neural network nonlinear least squares (HNNNLS). The live data of a metalized scale model B-52 aircraft mounted on a rotating platform in a microwave anechoic chamber and a flying Boeing-727 aircraft were processed. The imaging results indicate that, compared to the conventional Fourier method, either higher resolution for the same effective bandwidth of transmitted signals and total rotation angle in imaging or equal-quahty images from smaller bandwidth and total rotation angle may be obtained by these superresolution approaches. Moreover, these methods are compared in respect of their resolution capability and computational complexity.