Resource-Efficient Range-Doppler Map Generation Using Deep Learning Network for Automotive Radar Systems

Abstract

In this paper, we present a deep neural network aimed at enhancing the resolution of range-Doppler (RD) maps in frequency-modulated continuous wave radar systems. The proposed deep neural network consists of an U-net-based generator and a discriminator. The low-resolution (LR) RD map is processed through the generator, resulting in a super-resolution (SR) RD map. Then, the discriminator compares the SR RD map obtained from the generator with ground truth high-resolution (HR) RD map. Finally, the generator continuously trains until the loss between the two RD maps is minimized. The efficacy of the proposed method has been verified through simulations and real-world measurements. When compared with the ground truth HR RD map, the generated SR RD map by proposed method showed only 5.24% increase in pixel-wise mean squared error and a 0.477% decrease in peak signal-to-noise ratio. Through the proposed method, target detection and tracking performance can be improved by efficiently operating radar resources.