Polarimetric Radar Cross-Sections of Pedestrians at Automotive Radar Frequencies
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Abstract (arXiv)
Simulation of radar cross-sections (RCS) of pedestrians at automotive radar frequencies forms a key tool for software verification test beds for advanced driver assistance systems. Two commonly used simulation methods are: the computationally simple scattering center model of dynamic humans; and the shooting and bouncing ray technique based on geometric optics. The latter technique is more accurate but due to its computational complexity, it is usually used only for modeling scattered returns of still human poses. In this work, we combine the two methods in a linear regression framework to accurately estimate the scattering coefficients or reflectivies of point scatterers in a realistic automotive radar signal model which we subsequently use to simulate range-time, Doppler-time and range-Doppler radar signatures. The simulated signatures show a normalized mean square error below 10% and a structural similarity above 81% with respect to measurement results generated with an automotive radar at 77 GHz.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2019.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 1.
3 papers.
- 2021A Multi-Layered Approach for Measuring the Simulation-to-Reality Gap of Radar Perception for Autonomous Drivingcited by 7
- 2021Deep Evaluation Metric: Learning to Evaluate Simulated Radar Point Clouds for Virtual Testing of Autonomous Drivingcited by 3
- 2024MAROON: A Dataset for the Joint Characterization of Near-Field High-Resolution Radio-Frequency and Optical Depth Imaging Techniquescited by 1
- 2024MAROON: A Dataset for the Joint Characterization of Near-Field High-Resolution Radio-Frequency and Optical Depth Imaging Techniques1 Nov 2024
- 2021A Multi-Layered Approach for Measuring the Simulation-to-Reality Gap of Radar Perception for Autonomous Driving15 Jun 2021
- 2021Deep Evaluation Metric: Learning to Evaluate Simulated Radar Point Clouds for Virtual Testing of Autonomous Driving14 Apr 2021