GPU-based Parallel Implementation of SAR Imaging

Synthetic Aperture Radar (SAR) is an all-weather remote sensing technology and occupies a great position in disaster observation and geological mapping. The main challenge for SAR processing is the huge volume of raw data, which demands tremendous computation. This limits the utilization of SAR, especially for real-time applications. On the other hand, recent developments in Graphics Processing Unit (GPU) technology, which obtain general processing capability, high parallel computation performance, and ultra wide memory bandwidth, offer a novel method for computationally intensive applications. This work proposes a parallel implementation of SAR imaging on GPU via Compute Unified Device Architecture (CUDA), and provides a potential solution for SAR real-time processing. The results show that the proposed method obtained a speedup of 31.72, compared to a CPU platform.