Robust methods for the motion compensation of Inverse Synthetic Aperture Radar imagery

Techniques for improving stepped-frequency Inverse Synthetic Aperture Radar (ISAR) imagery are presented. Using these techniques one can efficiently focus the image to alleviate the problem of target recognition. Image improvement begins in the frequency domain where the echo phase can be adjusted to compensate for radial and rotational motion. Phase-profile averaging and range-profile cross-correlation correct the blurring caused by radial motion. Polar reformatting corrects the distortion produced by wide-aspect change. Image entropy minimization can correct the blurring caused by both types of motion.; Computational algorithms for these techniques are discussed. The Fast Fourier Transform (FFT) is used for conventional range-Doppler processing, and for phase-profile averaging. A high-speed correlation technique based on the FFT is used for range-profile cross-correlation. A golden-section-search method is used to determine the minimum entropy. Data generation models and experimental data are used to determine the limitations of each motion-compensation technique.; Phase-profile averaging and range-profile correlation may find applications in an open-loop mode that reduces the amount of signal processing. Entropy minimization is a prospective candidate for ISAR imagery automation, by using a closed-loop mode that optimizes image focusing.