2025/11/03 by Simin Zhu, Zhu, Simin, Satish Ravindran +7
Engineering · #Advanced SAR Imaging Techniques #FOS: Electrical engineering #Non-Invasive Vital Sign Monitoring #Radar Systems and Signal Processing #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2511.01431
openalex publication_date 2025/11/03 · openalex created_date 2025/11/06 · openalex updated_date 2026/07/28
The robust estimation of the mounting angle for millimeter-wave automotive radars installed on moving vehicles is investigated. We propose a novel signal processing pipeline that combines radar and inertial measurement unit (IMU) data to achieve accurate and reliable performance in realistic driving scenarios. Unlike previous studies, the method employs neural networks to process sparse and noisy radar measurements, reject detections from moving objects, and estimate radar motion. In addition, a measurement model is introduced to correct IMU bias and scale factor errors. Using vehicle kinematics, the radar mounting angle is then computed from the estimated radar motion and the vehicle's yaw rate. To benchmark performance, the proposed approach is comprehensively compared with two problem formulations and four estimation techniques reported in the literature. Validation is carried out on the challenging RadarScenes dataset, covering over 79 km of real-world driving. Results show that the proposed method achieves state-of-the-art accuracy and robustness, with reliable estimates obtained within approximately 25 seconds of driving. To the best of our knowledge, this is the first study to demonstrate that automotive radar mounting angles can be accurately estimated in complex, real traffic conditions, without requiring controlled environments, dedicated targets, or specially designed driving routes.