Translational Motion Compensation for ISAR Images Through a Multicriteria Decision Using Surrogate-Based Optimization

Inverse synthetic aperture radar (ISAR) image is constructed using 2-D spatial distributions of the radar cross section of a target. ISAR data gathered from moving targets might include interphase error that causes a blurry effect in the ISAR image. In this article, an efficient motion compensation (MC) scheme depending on multicriteria decision using surrogate-based optimization (SbO) for minimizing the entropy and maximizing the sharpness of the images is proposed to remove the blur from the images. In order to provide a multicriteria decision, Pareto optimality is exploited to balance two criteria of the entropy and sharpness synchronously. A signal with an interphase error is input to the MC system for determining the global optimal motion parameters of the velocity and acceleration so as to focus on the ISAR image. The proposed scheme is implemented to four simulated ISAR scenarios reported elsewhere through two aircraft models for performance demonstration and comparison with artificial bee colony (ABC), differential evolution (DE), and particle swarm optimization (PSO) implemented in the literature. It is pointed out that the proposed scheme is more successful and efficient in view of the image focusing quality as well as the numerical results such as the motion parameters, the entropy and sharpness, and structural similarity (SSIM) index. The results also show that the SbO outperforms the other methods as the velocity and acceleration increase.