Point Target-Based Autofocus SAR Imaging Algorithm with Visualization

This paper presents a point target-based autofocus Synthetic Aperture Radar (SAR) imaging algorithm. The algorithm employs graphical visualization to illustrate both the theoretical principles and implementation steps. Key implementation aspects include: - Phase error estimation and compensation through iterative refinement - Range-Doppler algorithm integration with autofocus modifications - Point target response analysis for quality assessment The algorithm demonstrates significant improvements over conventional methods by enhancing target information extraction accuracy and overall image quality through: - Adaptive phase gradient autofocus (PGA) implementation - Minimum entropy optimization techniques - Coherent processing improvements for better sidelobe suppression Application prospects in SAR imaging technology include potential integration with motion compensation systems and real-time processing architectures. The algorithm contributes to SAR development by providing more reliable imaging results for both civilian and military applications, with particular relevance to high-resolution imaging scenarios requiring precise phase correction. Code implementation typically involves MATLAB or Python-based processing chains with specialized functions for: - Range compression and azimuth processing - Phase error estimation loops - Image quality metrics calculation - Interactive visualization tools for algorithm performance monitoring