A Backprojection-Based Autofocus Imaging Method for Circular Synthetic Aperture Radar

Abstract

Circular synthetic aperture radar (CSAR) can obtain higher image resolution and more target information through 360° observation of the target. However, CSAR is often subject to motion errors due to the sensor’s accuracy limitations and the unique trajectory of the system. The autofocusing back projection algorithm based on the optimization of image sharpness compensates for motion errors through phase-error estimation. This method can attain relatively good performance while assuming the same error for all pixels; it ignores the spatial variance of motion errors. In order to solve this problem, this paper proposes an autofocusing method based on the Prewitt operator and particle swarm optimization (PSO), which first extracts the subaperture image feature points as a new dataset using the Prewitt operator, estimates the radar slope error on the new dataset using PSO, and subsequently compensates the subaperture image. Finally, the subaperture images are synthesized using an image-alignment method. The results of both the simulated experiments and the measured data validate the effectiveness of this method.

Position and Orientation System Error Analysis and Motion Compensation Method Based on Acceleration Information for Circular Synthetic Aperture Radar

“…In 2018, Kan et al [30] quantitatively analyzed the peak-drop coefficient of the target in the presence of motion errors and assessed the impact of motion errors on imaging quality based on the peak-drop coefficient. In 2023, Li et al [31] proposed an autofocus method based on the Prewitt operator and particle swarm optimization (PSO). This method aims to enhance imaging quality by optimizing the autofocus process using the Prewitt operator and the PSO algorithm.…”