Classification of Radar Targets Using Neural Networks on Systems-on-Chip

Abstract

The fast paced nature of modern combat situations has increased the need for quick and adaptable radar classifications to identify potential threats. One potential option to increase accuracy and performance is to introduce machine learning in radar classification tasks. Utilising convolutional neural networks to identify patterns within radar data provides an additional stream of information that could be used to classify targets. These neural nets require high-performance processing whilst still conforming to the low power and mobility requirements inherent in defence applications. These requirements make FPGAs a natural choice to be used as a hardware platform in radar classification tasks. This project explores the capabilities of the new Xilinx Versal VCK190 ACAP which combines regular FPGA architecture with AI Cores, which can be used to accelerate neural network tasks. Our findings show that the amount of radar classifications per second can be increased by at least 20x compared to a neural net running on a consumer grade CPU. This increase was achieved by utilising the low latency interfaces and high performance acceleration of the AI cores which are unique to the new Versal platform. These aspects make the VCK190 an interesting platform to further develop upon but more research needs to be made to improve the accuracy of the model.