汽车毫米波雷达干扰消除与信号重构方法综述*

doi:10.12267/j.issn.2096-5931.2025.06.014

摘要/Abstract

摘要：

随着汽车毫米波雷达系统在车辆主动安全和自动驾驶领域的广泛应用,传感器之间相互干扰问题也愈发突出。干扰消除与信号重构方法通过分析信号的特点和干扰的特性,利用算法分别进行干扰消除和信号重构,以提高干扰环境下信号的质量和可靠性。基于此,分别从干扰检测与信号重构两个角度对主流技术方法进行了系统归纳与分析。

关键词: 汽车毫米波雷达, 抗干扰, 信号处理, 干扰消除与信号重构

Abstract:

With the wide application of automotive millimeter-wave radar systems in the field of vehicle active safety and autonomous driving, the problem of mutual interference between sensors has become more and more prominent. Interference cancellation and signal reconstruction methods utilize algorithms for interference cancellation and signal reconstruction respectively by analyzing the characteristics of the signal and the interference in order to improve the quality and reliability of the signal under the interference environment. This paper systematically summarizes and analyzes the mainstream technical methods from the perspectives of interference detection and signal reconstruction, respectively.

Key words: automotive millimeter-wave radar, anti-interference, signal processing, interference cancellation and signal reconstruction

中图分类号:

TN958

夏青波, 韩笑, 施佳佳. 汽车毫米波雷达干扰消除与信号重构方法综述^*[J]. 信息通信技术与政策, 2025, 51(6): 87-96.

XIA Qingbo, HAN Xiao, SHI Jiajia. A review of interference cancellation and signal reconstruction methods for automotive millimeter-wave radar[J]. Information and Communications Technology and Policy, 2025, 51(6): 87-96.

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链接本文:

http://ictp.caict.ac.cn/CN/10.12267/j.issn.2096-5931.2025.06.014

http://ictp.caict.ac.cn/CN/Y2025/V51/I6/87

图/表 6

参考文献 32

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方法	特点
阈值设置与窗口函数加权	设置阈值和窗口函数加权检测脉冲噪声并提出相位噪声抑制方法
小波变换与低通滤波器	利用小波变换和低通滤波器计算阈值
CFAR阈值化	通过能量尖峰检测和CFAR阈值化方法检测干扰
线性阈值初始化	初始化线性阈值并逐步降低阈值恢复样本
样本计算与迭代更新	计算信号样本确定阈值并迭代更新抑制干扰
迭代阈值	通过迭代阈值检测弱干扰信号
一阶差分与阈值检测	采用一阶差分和设定阈值检测变化部分
2D CA-CFAR与2D IAA	使用2D CA-CFAR检测干扰并置零,2D IAA估计图像,RELAX优化
单元平均CFAR检测器	调节参数和扩张操作提高检测准确性
OS-CFAR与卡尔曼滤波器	使用OS-CFAR识别干扰区域卡尔曼滤波器恢复信号
ITS-CFAR	动态调整阈值提高检测性能
SS-CFAR	抑制目标旁瓣精确检测干扰
干扰感知与重建	关闭信号传输感知干扰重建干扰复制抑制窄带干扰
IMCA与APVMD	IMCA和APVMD算法检测和抑制干扰
MMSE与LP-FIR滤波器	设计LP-FIR滤波器获取干扰信号包络
改进PELT算法	计算最优惩罚因子来准确检测干扰位置
RNN与Adam优化	使用RNN检测干扰GRU提高检测精度
卷积编码器	模拟干扰生成并生成掩码并检测干扰样本位置
SeparationNet神经网络	基于DNN架构重构多通道信号提取特征
FCDNN与LSTM	对比使用FCDNN和LSTM检测器

方法	特点
阈值设置与窗口函数加权	设置阈值和窗口函数加权检测脉冲噪声并提出相位噪声抑制方法
小波变换与低通滤波器	利用小波变换和低通滤波器计算阈值
CFAR阈值化	通过能量尖峰检测和CFAR阈值化方法检测干扰
线性阈值初始化	初始化线性阈值并逐步降低阈值恢复样本
样本计算与迭代更新	计算信号样本确定阈值并迭代更新抑制干扰
迭代阈值	通过迭代阈值检测弱干扰信号
一阶差分与阈值检测	采用一阶差分和设定阈值检测变化部分
2D CA-CFAR与2D IAA	使用2D CA-CFAR检测干扰并置零,2D IAA估计图像,RELAX优化
单元平均CFAR检测器	调节参数和扩张操作提高检测准确性
OS-CFAR与卡尔曼滤波器	使用OS-CFAR识别干扰区域卡尔曼滤波器恢复信号
ITS-CFAR	动态调整阈值提高检测性能
SS-CFAR	抑制目标旁瓣精确检测干扰
干扰感知与重建	关闭信号传输感知干扰重建干扰复制抑制窄带干扰
IMCA与APVMD	IMCA和APVMD算法检测和抑制干扰
MMSE与LP-FIR滤波器	设计LP-FIR滤波器获取干扰信号包络
改进PELT算法	计算最优惩罚因子来准确检测干扰位置
RNN与Adam优化	使用RNN检测干扰GRU提高检测精度
卷积编码器	模拟干扰生成并生成掩码并检测干扰样本位置
SeparationNet神经网络	基于DNN架构重构多通道信号提取特征
FCDNN与LSTM	对比使用FCDNN和LSTM检测器