Analyzing radar cross section signatures at automotive microwave radar

Zizheng Wang; Feng Qi; Hongming Wu

doi:10.1117/12.2691679

6 November 2023 Analyzing radar cross section signatures at automotive microwave radar

Zizheng Wang, Feng Qi, Hongming Wu

Proceedings Volume 12921, Third International Computing Imaging Conference (CITA 2023); 129213X (2023) https://doi.org/10.1117/12.2691679
Event: Third International Computing Imaging Conference (CITA 2023), 2023, Sydney, Australia

Abstract

With the increasing demand for autonomous vehicles, the reliable detection and recognition of objects have become paramount. Millimeter-wave radar systems operating at frequencies such as 94GHz offer several advantages, including high resolution, precise range measurement, and resilience to adverse weather conditions. However, accurately characterizing the RCS of objects at this frequency range is essential to optimize the performance of these radar systems. In this work, we present Radar Cross Section (RCS) measurements of various objects commonly encountered on the road such as the metal balls, the bricks. The acquired findings illuminate the radar signal fluctuations demonstrated by different entities at a frequency of 94GHz. This knowledge serves as a foundation for developing robust algorithms and signal processing techniques that enhance the object detection, classification, and tracking capabilities of radar-based autonomous driving systems. Moreover, our research provides a valuable dataset of RCS measurements for different objects at 94GHz, which can serve as a benchmark for future studies in the field of radar system design and testing. The availability of such a dataset facilitates the development and validation of radar system models, the evaluation of sensor fusion approaches, and the comparison of performance across different radar hardware and signal processing algorithms. The results contribute to the advancement of radar-based autonomous driving systems by providing crucial insights into the scattering characteristics of objects at millimeter-wave frequencies. The findings have practical implications for optimizing the design, testing, and performance of radar systems, ultimately enhancing the safety and efficiency of autonomous vehicles in real-world scenarios.

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Zizheng Wang, Feng Qi, and Hongming Wu "Analyzing radar cross section signatures at automotive microwave radar", Proc. SPIE 12921, Third International Computing Imaging Conference (CITA 2023), 129213X (6 November 2023); https://doi.org/10.1117/12.2691679

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KEYWORDS

Radar

Automotive radar

Algorithm development

Autonomous driving

Object detection

Radar signal processing

Signal processing

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