High-Success-Rate and Fast ISAR Imaging of Nonstationary Moving Platform-Attitude Rapidly Changing Ship Target With DS Evidence and Minimum Entropy Theory

Abstract

The acquisition of a well-focused and high-resolution inverse synthetic aperture radar (ISAR) image of a ship target is crucial for accurate target classification and recognition. In practical scenarios, ship targets exhibit complex maneuverability, and radar platforms demonstrate nonstationary behavior, which poses a serious challenge to conventional ISAR imaging algorithms. To address this problem, this article proposes a high-success-rate and fast ISAR imaging algorithm of nonstationary moving platform-attitude rapidly changing ship target (NSMP-ARCST) with Dempster-Shafer (DS) evidence and minimum entropy theory. The proposed algorithm employs multiple metrics to evaluate the imaging results and leverages the DS evidence theory to fuse these metrics for optimal imaging time interval (OITI) selection, aiming to enhance the success rate and robustness of ISAR imaging. Furthermore, this article introduces an improved fixed-point iterative minimum entropy phase-adjustment (IFPI-MEPA) method to optimize the ISAR imaging quality and computational speed under low signal-to-noise ratio (SNR) conditions, which contributes to an increased success rate of OITI selection and reduced computational complexity, thereby endowing it with substantial practical applicability. Experimental results using both simulated and real measured data illustrate the effectiveness and robustness of the proposed algorithm.