Optimizations of Dual Polarization FMCW Weather Radar Signal Processing on CUDA Platform

Abstract

Weather radar is a system that utilizes advanced radio wave engineering to detect precipitation in the atmosphere. One of the wave generation technique used in weather radar is frequency-modulated continuous wave (FMCW), with dual polarization for differentiating detected precipitation types by its shape and size. Weather radar signal processing is usually performed using digital signal processing and field-programmable gate array (FPGA), that performs well but with difficulty in system development and deployment. Software implementation of weather radar signal processing enables easier and faster development and deployment with the cost of performance when done serially. Parallel implementation using general purpose graphics processing units (GP-GPU) may provide best of both worlds with easier development and deployment compared to hardware-based solutions but with better performance than serial CPU implementations. In this paper, implementation of various optimization strategies weather signal radar processing in GP-GPU environment on the Nvidia CUDA platform is shown. Performance measurements show that among optimization strategies implemented, only the utilization of multiple CUDA streams give significant performance gain. This paper contributes in attempts to build full weather radar signal processing stack on GPU.