摘要
汽车雷达信号处理的一个重要任务是可靠地探测汽车前方及周边的目标,通过比较雷达反射信号的频谱来判断频谱信号是否达到特定的检测门限值,使用恒定的门限值会导致大量虚警目标被检测,如果虚警目标太多,会使计算机的处理能力达到饱和,势必影响正常目标的检测。因此,一种自适应噪声阈值的恒虚警率(CFAR)算法被广泛的应用在雷达信号处理中。本文针对汽车FMCW雷达常用的两种CFAR算法,从原理上对算法进行阐述,并通过仿真软件和实验数据对算法性能进行分析比较。
An important task of automotive radar signal processing is to reliably detect the targets in front of and around the vehicle,and judge whether the spectrum signal reaches a specific detection threshold by comparing the spectrum of radar reflected signal.Using a constant threshold value will lead to a large number of false alarm targets being detected.If there are too many false alarm targets,the processing capacity of the computer will reach saturation,It is bound to affect the detection of normal targets.Therefore,a constant false alarm rate(CFAR)algorithm with adaptive noise threshold is widely used in radar signal processing.In this paper,two CFAR algorithms commonly used in automotive FMCW radar are described in principle,and the performance of the algorithm is analyzed and compared through simulation software and experimental data.
作者
陈海玲
徐靖
谭蓉凡
李海娟
郑休宁
Chen Hailing;Xu Jing;Tan Rongfan;Li Haijuan;Zheng Xiuning(Wuzhou Vocational College,Wuzhou Guangxi,543000)
出处
《电子测试》
2022年第5期61-64,共4页
Electronic Test
基金
广西高校中青年教师科研基础能力提升项目“基于AWR1243毫米波雷达传感器的目标参数测量实现(2021KY1489)”。
