Automotive Radar Performance in Environments with Multiple Interference Sources
Oren Longman, Guy Mardiks, Tomer Maayan, and Gaston Solodky

TL;DR
This paper analyzes automotive radar performance amid increasing interference from multiple sources, using simulations and experiments to evaluate mitigation techniques and highlight the need for scalable interference management.
Contribution
It introduces a comprehensive simulation framework and experimental validation for radar interference, assessing mitigation strategies in high-density environments.
Findings
Significant performance degradation occurs under high interference conditions.
Time-frequency coding offers robust detection even at high radar densities.
Current mitigation techniques have limitations, necessitating scalable solutions.
Abstract
Automotive radars are increasingly susceptible to mutual interference from neighboring radar systems, which can lead to false target detections and the masking of valid targets. While current interference levels remain manageable due to the relatively low penetration of radar-equipped vehicles, this assumption is expected to break down as radar adoption and per-vehicle radar density continue to increase. This paper presents a comprehensive analysis of automotive radar performance in high-density interference environments. A realistic end-to-end simulation framework is developed at the intermediate frequency (IF) level, incorporating analytical interference modeling and detailed radar signal processing. The study evaluates the impact of interference across a range of future scenarios characterized by increased radar density and multiple radar configurations per vehicle. Conventional…
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