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OFDM Waveform Design for Interference Resistant Automotive Radars

机译：用于干扰汽车雷达的OFDM波形设计

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摘要

Autonomous driving is experiencing a monumental technological shift, making it one of the most highly discussed topics of research. With novel technologies, like advanced driver assistance systems (ADAS), the automotive industry is making significant advancements in both driving comfort and safety. ADAS rely heavily on Frequency Modulated Continuous Waveform (FMCW) radar sensors for speed and range estimation. However, this technology suffers from interference management problem and an inherent ghost target detection with few specific waveforms. This paper introduces a new technique that utilizes Orthogonal Frequency-Division Multiplexing (OFDM) waveform to overcome ghost detection and increase robustness against interference. OFDM leverages maximum length sequence (MLS) (i.e., m-sequence) and provides orthogonality between different transmitted waveforms. Also, incorporating an appropriate scrambled sequence to the proposed waveform further reduces the impact of interference and improves the reliability of target detection. Simulations were conducted to demonstrate proposed algorithm performance for different scenarios, like single- and multiple-neighbors. Furthermore, results were compared and analyzed relative to conventional FMCW in similar scenarios.
机译：自主驾驶正在经历一个巨大的技术转变，使其成为最讨论的研究主题之一。具有新颖的技术，如先进的驾驶辅助系统（ADAS），汽车行业在驾驶舒适和安全方面取得了重大进展。 ADA严重依赖于频率调制的连续波形（FMCW）雷达传感器，用于速度和范围估计。然而，该技术遭受干扰管理问题，并且具有少量特定波形的固有重影目标检测。本文介绍了一种利用正交频分复用（OFDM）波形来克服Ghost检测的新技术，并增加防止干扰的鲁棒性。 OFDM利用最大长度序列（MLS）（即，M序列），并在不同传输波形之间提供正交性。而且，将适当的加扰序列纳入所提出的波形进一步降低了干扰的影响并提高了目标检测的可靠性。进行仿真以证明不同场景的提议算法性能，如单个和多邻居。此外，在类似场景中的传统FMCW进行比较和分析结果。

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