汽车雷达信号处理

D. Kok, J. Fu
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引用次数: 26

摘要

随着事故率的上升,研究人员正在寻找减少死亡人数的解决方案。一些公司改进了汽车设计,以更充分地保护司机。有些人建议改善交通和道路系统,以减少事故发生的机会。还有人建议安装特殊装置,以提高司机的态势感知能力,并提醒他们注意危险情况。机动车辆可以配备雷达传感器,用于检查车辆周围的空间环境。由于它的全天候能力,雷达是为此目的最常采用的传感器。本文采用瑞萨SH7615发动机开发板,开发并实现了77 ghz FMCW汽车雷达信号处理器。该项目包括构建额外所需的硬件、DSP算法和数据模拟器程序,以生成测试数据。汽车雷达信号处理器的主要目的是从无用的杂波(如路面)中检测出合法的目标,并从雷达回波中提取目标信息。本文还介绍了新型与或CFAR的应用。给出了与或CFAR的简单数学融合模型,并对其进行了说明。这份手稿分为几节。第一部分将深入研究汽车雷达的基础知识和构建模块。这项工作的信号处理部分将在第二节中介绍。第三部分将解释为模拟测试数据而构建的数据模拟器。第四部分给出了测试结果。最后一节提出了从经验中得出的结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Signal processing for automotive radar
With rising accident rates, researchers are looking for solutions to reduce fatalities. Some enhance car designs to protect drivers more adequately. Some propose improvements to the traffic and road systems to reduce the chances of accidents. Still others propose the installation of special gadgets to improve the situational awareness of drivers and to alert them to dangerous circumstances. A motor vehicle may be equipped with a radar sensor that checks the spatial environment around the vehicle. The radar is the most commonly adopted sensor for this purpose due to its all-weather capability. In this paper, a 77-GHz FMCW automotive radar signal processor is developed and implemented using the Renesas (previously Hitachi) SH7615 solutions engine development board. The project includes building additional required hardware, the DSP algorithm and a data simulator program to generate test data for testing. The main purpose of the automotive radar signal processor is to detect legitimate targets from unwanted clutter (e.g. road surfaces), and to extract target information from the radar returns. In this paper, the application of the new AND-OR CFAR is also introduced. The simple mathematical fusion models of the AND-OR CFAR are provided and explained here as well. This manuscript is divided into sections. The first section will delve into the basics and the building blocks of the automotive radar. The signal processing portion of this work will be presented in the second section. The data simulator built to simulate testing data is explained in the third section. Results from testing are put forward in section four. Conclusions drawn from the experience are presented in the last section.
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