Airborne Synthetic Aperture Radar Auto-focusing Algorithms with Point Target Simulation

This text discusses airborne synthetic aperture radar (SAR) auto-focusing algorithms implemented through point target simulation rather than using measured data. Notably, airborne SAR auto-focusing represents an advanced signal processing technique that utilizes multiple pulse echo data from synthetic aperture radar to achieve high-resolution imaging. The core algorithm typically involves phase error estimation and compensation through methods like Phase Gradient Autofocus (PGA) or Map-Drift, implemented via cross-correlation operations and Fourier transforms in MATLAB or Python. While actual measured data is absent in this study, the simulation approach validates the algorithm's robustness by generating ideal point targets with controlled phase errors. With proper measured data support, these algorithms would demonstrate more reliable and accurate performance in practical scenarios. Further research and experimentation involving real SAR data collection and advanced optimization techniques will help verify and improve airborne SAR auto-focusing algorithm performance to meet diverse practical application requirements, particularly in motion compensation and high-precision imaging applications.