Improving Recognition of Road Users via Doppler Radar Data and Deep Learning Convolutional Networks

Abstract

This research presents findings from laboratory experiments on a novel method for identifying and differentiating objects using radar signatures and a specialized convolutional neural network architecture. Previously introduced by the authors, this method has been validated through real-world measurements in an urban environment with a 24 GHz frequency-modulated continuous-wave radar. This study describes how radar signatures, generated in the MATLAB (R2023b) environment from I and Q signals captured by the uRAD USB v1.2 radar, were processed. A database of radar signatures for pedestrians, cyclists, and vehicles was created, and a tailored convolutional neural network was trained. The developed solution achieves an accuracy of over 95% in distinguishing between various objects. The simulation results and successful tests support the application of this system across various sectors. The key innovations include distinguishing multiple objects from a single radar signature, a custom architecture for the convolutional neural network, and an application that processes radar data to produce near-real-time recognition results.