Target Detection Method for Frequency-Modulated Continuous-Wave Radars Using 2D Truncated Statistic CFAR Technique

doi:10.7652/xjtuxb201710019

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Target Detection Method for Frequency-Modulated Continuous-Wave Radars Using 2D Truncated Statistic CFAR Technique

更新时间：2025-07-09

- Target Detection Method for Frequency-Modulated Continuous-Wave Radars Using 2D Truncated Statistic CFAR Technique
- Vol. 51, Issue 10, Pages: 113-119(2017)
- 作者机构：
  
  南京理工大学电子工程与光电技术学院,南京,210094
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201710019
  CLC： TN958.94
- Online First：10 October 2017，
  
  Published：2017
- 稿件说明：
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Target Detection Method for Frequency-Modulated Continuous-Wave Radars Using 2D Truncated Statistic CFAR Technique[J]. 2017, 51(10): 113-119.
DOI：

Target Detection Method for Frequency-Modulated Continuous-Wave Radars Using 2D Truncated Statistic CFAR Technique[J]. 2017, 51(10): 113-119. DOI： 10.7652/xjtuxb201710019.

摘要

针对调频连续波(FMCW)雷达在密集多目标环境中检测能力降低的问题

提出了采用右截断瑞利分布模型的二维截断统计量恒虚警(TS-CFAR)检测方法。首先对目标回波信号进行二维快速傅里叶变换(FFT)得到二维幅度谱

接着采用二维参考窗在幅度谱上滑动以剔除二维滑窗内幅度过高的参考单元

进而采用右截断瑞利分布模型描述剩余参考单元

最后采用基于最大似然估计的查表法估计门限参量并进行目标检测。与FMCW雷达常用的有序统计量恒虚警(OS-CFAR)和单元平均恒虚警(CA-CFAR)方法相比

TS-CFAR方法在密集多目标环境中检测能力更强。仿真分析表明

当参考窗内有6个干扰目标且检测概率为0.9时

TS-CFAR方法的CFAR损失比CA-CFAR方法小3 dB

比OS-CFAR方法小0.8 dB

而且由于在运算过程中采用了查表法

TS-CFAR方法的计算量远小于OS-CFAR方法。实验结果验证了TS-CFAR方法对FMCW雷达的目标检测的有效性。

Abstract

A new type of detection method using the truncated statistic(TS)constant false alarm rate(CFAR)technique is proposed based on the singly right truncated Rayleigh distribution model and to solve the problem that the target detection performance of the frequency-modulated continuous-wave(FMCW)radar degrades in high-target-density situations. The first step of the method applies the two-dimensional(2D)fast Fourier transform(FFT)to transform the radar beat signal into a 2D FFT magnitude spectrum. Then a 2D window is used to slide on the 2D magnitude spectrum

and outliers in the 2D sliding window are eliminated. The singly right truncated Rayleigh distribution model is used to model the remaining background samples. Finally

the look-up table method based on the maximum-likelihood(ML)estimation is adopted to estimate the required threshold parameter

and the target detection is realized. The TS-CFAR detection method has more superior anti-interference performance than the ordered statistic(OS)CFAR and cell averaging(CA)CFAR methods

which are commonly applied in automotive FMCW radars. Simulation results show that the CFAR loss of the TS-CFAR is 3 dB and 0.8 dB lower than the CA-CFAR method and the OS-CFAR method

respectively

when there are six interfering targets in the reference window and the target detection probability is 0.9. Moreover

the proposed method has much lower computational complexity compared with the OS-CFAR method since the look-up table approach is applied.

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Keywords

references

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