Research on optimizing fast SAR echo imaging algorithms for real-time processing

The integration of intelligent algorithms and high-performance computing technologies has opened new possibilities for achieving real-time synthetic aperture radar (SAR) imaging. Traditional time-domain Back Projection (BP) and frequency-domain Range-Doppler Algorithm (RDA) methods often suffer from high computational complexity, memory-intensive interpolation procedures, and limited scalability when processing large-scene or high-resolution SAR echo data. To address these challenges, this study proposes an optimized fast SAR echo imaging model specifically designed for real-time operation. The framework adopts a hybrid time-domain/frequency-domain architecture that incorporates GPU-accelerated parallel computing, a pipelined FFT structure, and an enhanced Stolt interpolation strategy based on segmented polynomial approximation. These improvements effectively reduce redundant memory access, suppress interpolation distortion, and enhance phase-compensation precision. Experimental evaluations on a 512×512 imaging configuration show that the optimized model decreases the average processing time from 21.9 s to 10.7 s, while GPU utilization increases to 87.9%. Imaging quality is also improved, with the peak signal-to-noise ratio (PSNR) enhanced by approximately 2.5 dB and energy concentration exceeding 90%. The results verify the model’s capability to maintain high-resolution focusing performance under stringent latency requirements, demonstrating its strong suitability for real-time SAR imaging applications in airborne and spaceborne platforms.