A Deep Weighted CFAR Detector for Automotive Radar
S. Jia (TU Delft - Electrical Engineering, Mathematics and Computer Science)
F. Fioranelli – Mentor (Microwave Sensing, Signals & Systems)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Automotive radar is a key all-weather sensor for autonomous driving, providing range and radial-velocity measurements under adverse illumination and weather conditions. Target detection and subsequent point-cloud generation are therefore critical stages in automotive radar signal processing. However, conventional constant false alarm rate (CFAR) detectors rely on fixed local reference rules and statistical assumptions that are often violated by extended targets and non-homogeneous backgrounds in road scenes, resulting in sparse and spatially inaccurate point clouds. End-to-end deep-learning detectors can exploit richer radar context, but may require substantial computational resources and provide limited interpretability and post-training control of the detection threshold.
In this work, we propose a hybrid neural CFAR-like detector in which a three-level U-Net estimates a background-reliability map from radar data, and an explicit local thresholding stage uses this map to form cell-wise detection decisions. Evaluated experimentally on the \textit{RaDelft} dataset, the proposed detector achieved the lowest Chamfer Distance (CD) among the evaluated methods, reaching 1.36 m compared with 3.19 m for CA-CFAR, 3.29 m for OS-CFAR, and 1.60 m for a deep-learning baseline taken from the literature. Moreover, the proposed method used 0.87 million parameters and achieved 10.58 GFLOPs, which is 13 times less than the evaluated deep-learning baseline.
These results indicate that learning the reliability of local background samples can improve automotive radar point-cloud quality while retaining an explicit and adjustable detection procedure.