High-Precision GPU-Accelerated Simulation Algorithm for Targets under Non-Uniform Cluttered Backgrounds

Abstract

This article presents a high-precision airborne video synthetic aperture radar (SAR) raw echo simulation method aimed at addressing the issue of simulation accuracy in video SAR image generation. The proposed method employs separate techniques for simulating targets and ground clutter, utilizing pre-existing SAR images for clutter simulation and employing the shooting and bouncing rays (SBR) approach to generate target echoes. Additionally, the method accounts for target-generated shadows to enhance the realism of the simulation results. The fast simulation algorithm is implemented using the C++ programming language and the Accelerated Massive Parallelism (AMP) framework, providing a fusion technique for integrating clutter and target simulations. By combining the two types of simulated data to form the final SAR image, the method achieves efficient and accurate simulation technology. Experimental results demonstrate that this method not only improves computational speed but also ensures the accuracy and stability of the simulation outcomes. This research holds significant implications for the development of algorithms pertaining to video SAR target detection and tracking, providing robust support for practical applications.

Miniaturization Design of High-Integration Unmanned Aerial Vehicle-Borne Video Synthetic Aperture Radar Real-Time Imaging Processing Component

“…The high integration of ViSAR real-time imaging processing components also puts higher demands on the computing power of hardware platforms. Currently, the research on onboard real-time processing mainly involves three commonly used platforms, i.e., digital signal processing (DSP) [17], graphic processing unit (GPU) [18][19][20], and FPGA [21][22][23][24][25]. In 2022, Wang et al reported a terahertz SAR imaging processing system based on a multi-core DSP.…”