High-Performance Beamforming for Radar Technology : A Comparative Study of GPU Beamforming Algorithms

Sammanfattning

Radar technology is widely used in today´s society, whether it is the localisation and identification of aircraft in air traffic control systems, ships in harbour management systems, or the weather forecast presented on the news. In military applications, such as in fighter jets or missile lock-on systems, the speed at which the radar processes incoming data is essential to ensure a successful outcome. These applications need more powerful execution platforms, such as Application-Specific Integrated Circuits (ASICs) or Field-Programmable Gate Array (FPGAs) to run at the required speed. Radar technology manufacturers face both high development costs and long development cycles when designing, redesigning and developing these powerful execution platforms. A solution to these problems could be to design and develop the system in software before implementing it on the specialised ASIC and FPGA ships. As a step toward creating a software-based processing chain for radar systems, this thesis amide to investigate whether General-Purpose Graphical Processing Units (GPUs) could be used to develop and run beamforming applications in real-time at a sample rate of 100 MHz. To achieve this goal, the Bartlett and MVDR Beamforming algorithms were analysed and implemented in the Computer Unified Device Architecture (CUDA) programming framework on an NVIDIA GeForce RTX 2080 super GPU. The algorithms´ parallelisable elements were considered before implementation, and then implemented to fit with the CUDA programming model. In the end, both beamformers´ primary acceleration methods is using the cuBLAS library for linear algebra operations. The results of this study show that beamforming in real-time is not possible on a GPU when processing a signal sampled at 100 MHz. However, the GPU used just passed its third anniversary since release, and NVIDIA has released subsequent generations of GPUs with significant (>100%) performance increases. Future work on this area could include trying the implementations on more recent GPUs or implementing other beamforming algorithms on GPUs.