Automotive Radar Performance in Environments with Multiple Interference Sources
汽车雷达在多重干扰源环境中的性能
Oren Longman, Guy Mardiks, Tomer Maayan, Gaston Solodky
AI总结 本文研究了高密度干扰环境下汽车雷达的性能,提出了一种端到端的仿真框架,评估了多种干扰场景对雷达性能的影响,并验证了传统干扰抑制技术的局限性,强调了未来需要协调和可扩展的干扰管理策略。
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汽车雷达正越来越容易受到邻近雷达系统的相互干扰,这可能导致虚假目标检测和有效目标的掩盖。尽管当前的干扰水平仍可管理,由于雷达车辆的渗透率相对较低,但这一假设预计随着雷达的普及和每辆车雷达密度的增加而崩溃。本文对高密度干扰环境下的汽车雷达性能进行了全面分析。在中间频段(IF)级别开发了一个现实的端到端仿真框架,结合了分析性干扰建模和详细的雷达信号处理。研究评估了干扰在一系列未来场景中的影响,这些场景以增加的雷达密度和每辆车的多雷达配置为特征。传统干扰抑制技术被系统地评估以验证仿真结果,通过使用暴露于多达30个干扰雷达的主机雷达,在消音和真实环境进行了受控实验。结果表明,在高干扰条件下性能显著下降,检测概率和有效范围有显著减少。在评估的技术中,时频编码始终提供最稳健的性能,即使在雷达渗透率较高时仍能保持较高的检测概率。这些发现突显了当前抑制方法的局限性,并强调了未来汽车雷达系统中协调和可扩展的干扰管理策略的重要性。
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 interference mitigation techniques are systematically assessed to validate the simulation results, controlled experiments were conducted using a host radar exposed to up to 30 interfering radars in both anechoic and real-world environments. The results demonstrate significant performance degradation under high interference conditions, with substantial reductions in detection probability and effective range. Among the evaluated techniques, time-frequency coding consistently provides the most robust performance, maintaining high detection probability even at elevated radar penetration rates. These findings highlight the limitations of current mitigation approaches and emphasize the need for coordinated and scalable interference management strategies in future automotive radar systems.