CFARNet: Learning-Based High-Resolution Multi-Target Detection for Rainbow Beam Radar

TL;DR

CFARNet introduces a learning-based framework using CNNs to improve multi-target detection and resolution in mmWave rainbow beam radar, outperforming traditional CFAR methods especially in low-SNR and dense target scenarios.

Contribution

The paper proposes CFARNet, a novel CNN-based peak detection method that replaces CFAR in radar processing, enabling high-resolution, robust multi-target detection in challenging conditions.

Findings

CFARNet significantly outperforms baseline methods in simulations.

It provides higher angular resolution and robustness in low-SNR environments.

CFARNet improves computational efficiency for real-time radar processing.

Abstract

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.