Millimeter-wave (mmWave) OFDM radar equipped with rainbow beamforming, enabled by phase-time arrays (PTAs), provides wide-angle coverage and is well-suited for fast real-time target detection and tracking. However, accurate detection of multiple closely spaced targets remains a key challenge for conventional signal processing pipelines, particularly those relying on constant false alarm rate (CFAR) detectors. This paper presents CFARNet, a learning-based processing framework that replaces CFAR with a convolutional neural network (CNN) for peak detection in the angle-Doppler domain. The network predicts target subcarrier indices, which guide angle estimation via a known frequency-angle mapping and enable high-resolution range and velocity estimation using the MUSIC algorithm. Extensive simulations demonstrate that CFARNet significantly outperforms a baseline combining CFAR and MUSIC, especially under low transmit power and dense multi-target conditions. The proposed method offers superior angular resolution, enhanced robustness in low-SNR scenarios, and improved computational efficiency, highlighting the potential of data-driven approaches for high-resolution mmWave radar sensing.
@article{arxiv.2505.10150,
title = {CFARNet: Learning-Based High-Resolution Multi-Target Detection for Rainbow Beam Radar},
author = {Qiushi Liang and Yeyue Cai and Jianhua Mo and Meixia Tao},
journal= {arXiv preprint arXiv:2505.10150},
year = {2026}
}
Comments
6 pages, 6 figures. V3: Updated simulation results with corrected transmit power settings and carrier frequency (77 GHz). Includes revised performance analysis using 90th percentile errors. This version provides more accurate evaluations than the previous conference version