An Enhanced Phase Gradient Autofocus Algorithm for SAR: A Fractional Fourier Transform Approach

Abstract

Synthetic aperture radar (SAR) technology is one of the imaging radar technologies receiving the most attention worldwide. The main purpose is to detect targets in the area of interest in different settings, such as day/night, various weather conditions, etc. Phase gradient autofocusing (PGA) algorithms have been widely used for autofocus in SAR imaging. Conventional PGA methods in stripmap SAR apply dechirping to switch the range-compressed phase history-domain signal to a form equivalent to that in spotlight mode. However, this switching method has inherent limitations in phase error estimation, leading to degraded autofocusing performance. To address this issue, we introduce an FrFT-based switching method that provides more precise and fast autofocus. Additionally, this method enables effective detection and extraction of moving targets in the environment where moving targets are present. Moving targets introduce additional phase errors that hinder accurate autofocus, making it essential to isolate and process them separately. We carried out practical experiments with an X-band chirp pulse SAR system to verify the proposed method and mount the system on an automobile.

Keywords

• synthetic aperture radar
• phase gradient autofocus
• fractional Fourier transform
• phase error correction