Deep Learning-Based Range-Doppler Map Reconstruction in Automotive Radar Systems

Abstract

In this paper, we consider the automotive orthogonal frequency division modulation-radar in millimeter wave band. To avoid interference between different radar systems, resources need to be split and then used by different radar systems. This thus degrades the radar performance as compared to the radar system having full resources (FRs). To mitigate this issue, we develop a deep learning-based range-Doppler (R-D) map reconstruction approach along with a time-frequency resource allocation scheme. In the reconstruction approach, we propose a deep learning-based convolutional neural network to reconstruct the R-D map such that the reconstructed R-D map can be close to the R-D map under FRs. In the resource allocation scheme, we propose a block-wise interleaved method that can facilitate the proposed reconstruction approach. Simulation results show that our proposed approach can effectively mitigate the performance degradation of radar systems when resources are shared among users.