Real-time imaging implementation of the Army Research Laboratory synchronous impulse reconstruction radar on a graphics processing unit architecture

Abstract

High computing requirements for the synchronous impulse reconstruction (SIRE) radar algorithm present a challenge for near real-time processing, particularly the calculations involved in output image formation. Forming an image requires a large number of parallel and independent floating-point computations. To reduce the processing time and exploit the abundant parallelism of image processing, a graphics processing unit (GPU) architecture is considered for the imaging algorithm. Widely available off the shelf, high-end GPUs offer inexpensive technology that exhibits great capacity of computing power in one card. To address the parallel nature of graphics processing, the GPU architecture is designed for high computational throughput realized through multiple computing resources to target data parallel applications. Due to a leveled or in some cases reduced clock frequency in mainstream single and multi-core general-purpose central processing units (CPUs), GPU computing is becoming a competitive option for compute-intensive radar imaging algorithm prototyping. We describe the translation and implementation of the SIRE radar backprojection image formation algorithm on a GPU platform. The programming model for GPU's parallel computing and hardware-specific memory optimizations are discussed in the paper. A considerable level of speedup is available from the GPU implementation resulting in processing at real-time acquisition speeds.