Robust ISAR Autofocus via Newton-Based Tsallis Entropy Minimization

Abstract

The autofocus technology constitutes a critical component in the process of inverse synthetic aperture radar (ISAR) imaging, as its performance significantly impacts the quality of the resulting radar imagery. Among existing autofocus techniques, those based on the minimum entropy criterion have demonstrated strong robustness and are widely applied in ISAR imaging applications. Nevertheless, the minimum Tsallis entropy-based autofocus (MTEA) method is often burdened with substantial computational demands, primarily due to the complex formulation of image entropy and the iterative search required for optimizing phase error correction. To address this limitation, this study presents a fast MTEA algorithm that incorporates the Newton method for efficient optimization. Additionally, the Levenberg–Marquardt (LM) modification is integrated into the MTEA framework to further enhance computational efficiency. Both the numerical analysis of computational complexity and experimental results indicate that the proposed method achieves a notable improvement in computational efficiency over the MTEA, while maintaining the focusing quality of the reconstructed images.