PERFORMANCE ANALYSIS OF ISAR AUTOFOCUS ALGORITHM BASED ON IMAGE P-NORM

Inverse Synthetic Aperture Radar (ISAR) provides high-resolution imagery of targets under all-weather and all-time conditions, which is of great practical value. However, radar systems may introduce high-order phase errors in echoes, leading to image defocusing. Existing autofocusing methods based on image quality optimization can effectively focus radar images, but their speed and accuracy are affected by the Image Quality Function (IQF). Current literature lacks comprehensive analysis of IQF. This paper analyzes the performance of autofocusing algorithms based on the image P-norm (IP), deriving two conclusions: optimizing different IQFs is equivalent to optimizing the IP, with varying P-values. And P-value affects the speed and accuracy of autofocusing algorithms-higher P-value speeds up the process, while lower P-value improves accuracy but makes optimization more challenging. Simulations confirm the reliability of these findings.