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摘要: 作为高级驾驶辅助系统(ADAS)核心之一的汽车毫米波雷达因其具有全天时、全天候、小型化、集成度高等优势，提供了关键的感知能力，逐渐成为国内外学者及厂商关注的焦点。汽车毫米波雷达以汽车作为平台，其核心性能指标主要有距离、速度、角度分辨率、视场范围等，此外，精度、成本、实时性、检测性能和体积也是需要考虑的关键问题。日益提升的性能需求给汽车毫米波雷达信号处理带来了诸多挑战。为了改进雷达性能以满足更严格的要求，雷达的信号处理技术是至关重要的一环。获取致密的雷达点云、生成精确的雷达成像结果、对抗多个雷达系统间的相互干扰是其中的重点，也是后续跟踪、识别等应用的基础。因此，该文从汽车毫米波雷达的实际应用出发，立足于信号处理的关键技术，总结了相关研究成果，主要讨论与车载毫米波雷达相关的以下主题： (1)点云成像处理；(2)合成孔径雷达成像处理；(3)互扰抑制。文章最后对国内外研究现状进行了总结，并展望未来汽车毫米波雷达的发展趋势，希望能给相关领域读者以启发。Abstract: As one of the core components of Advanced Driver Assistance Systems (ADAS), automotive millimeter-wave radar has become the focus of scholars and manufacturers at home and abroad because it has the advantages of all-day and all-weather operation, miniaturization, high integration, and key sensing capabilities. The core performance indicators of the automotive millimeter-wave radar are distance, speed, angular resolution, and field of view. Accuracy, cost, real-time and detection performance, and volume are the key issues to be considered. The increasing performance requirements pose several challenges for the signal processing of millimeter-wave radar systems. Radar signal processing technology is crucial for improving radar performance to meet more stringent requirements. Obtaining dense radar point clouds, generating accurate radar imaging results, and mitigating mutual interference among multiple radar systems are the key points and the foundation for subsequent tracking, recognition, and other applications. Therefore, this paper discusses the practical application of the automotive millimeter-wave radar system based on the key technologies of signal processing, summarizes relevant research results, and mainly discusses the topics of point cloud imaging processing, synthetic aperture radar imaging processing, and interference suppression. Finally, herein, we summarize the research status at home and abroad. Moreover, future development trends for automotive millimeter-wave radar systems are forecast with the hope of enlightening readers in related fields.
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图 1 毫米波雷达点云成像算法处理流程
Figure 1. The processing flow of millimeter-wave radar point cloud imaging algorithm
表 1 基于信号处理方法实现点云成像相关论文汇总
Table 1. Summary of relevant papers of realization of point cloud imaging based on signal processing
作者 特点 雷达参数 效果 Li等人[2] BPSK-MIMO雷达 雷达工作频率为79 GHz，带宽为1.6 GHz 能对路沿、排水渠、停车场等场景成像，
具备一定的测高能力Qian等人[3] 基于SAR成像的汽车
雷达系统MilliPoint两片级联，6发8收的天线阵列，距离分辨率为0.044 m，最大探测距离11.31 m 对目标构建出密度和分辨率更高的三维点云 Engels
等人[4]高性能4片芯片级联雷达板 AWR2243，距离分辨率0.2 m，速度分辨率
0.1 m/s，方位角分辨率约等于1.4°，
俯仰角分辨率<3°在高速路场景下实现了目标检测，并验证了
雷达在恶劣工作环境的工作性能林凤泰等人[5] 提出基于最近迭代点的
多帧融合算法AWR2243，距离分辨率0.059 m，速度分辨率
0.055 m/s，角度分辨率1.4°使用多帧融合算法和自适应邻域半径DBSCAN
聚类算法得到场景相对稠密的点云数据Weishaupt
等人[6]提出了PreCFAR方法 雷达中心频率78 GHz，距离分辨率0.075 m，
速度分辨率0.15 m/s，角度分辨率3°改进了CFAR目标检测算法 Wei等人[7] 提出了Area-Based CFAR方法 文章未提及 大大提升了CFAR检测器性能，生成了
更为密集的点云图像兰吕鸿康
等人[8]提出CFAR算法中门限系数
自适应设置方法AWR2243，距离分辨率0.15 m，方位角度
分辨率1.4°，俯仰角度分辨率18°生成了较为致密可信的毫米波雷达三维点云图像 
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表 2 基于深度学习方法实现点云处理相关论文汇总
Table 2. Summary of relevant papers of realization of point cloud processing based on deep learning
作者 特点 效果 Danzer等人[9] 将PointNets应用于二维雷达数据 对车辆进行了检测、分类和二维边界盒估计 Jin等人[10] 使用GMM进行雷达点云分割 GMM方法简单，对行人分类效果较好，
而对车辆分类效果较差Xu等人[11] 提出RANSAC算法实现道路边界估计 实现了距离雷达30 m之内的道路边界检测，精度达81.89% Jin等人[12] 提出了基于雷达点云数据的自动驾驶道路路线估计算法 所提方法的估计精度要优于基于网格图的CNN Cheng等人[13,14] 提出了基于激光雷达数据自动生成标签方法的
3D点云检测器RPDNet相较于CFAR检测器，RPDNet在抑制雷达杂波和
生成更密集点云方面具有更好的性能Jiang等人[15] 提出了CV-DCN 使用深度学习实现了单帧雷达数据角度超分辨 Franceschi等人[16] 采用两层级联神经网络架构替代CFAR和DOA估计 相较于传统的雷达信号处理方法生成更为密集的3D点云图像 Tan等人[17] 提出了一种用于目标检测的多帧毫米波雷达点云检测框架 多个实际道路场景下实现了对车辆的检测和识别 Sun等人[18,19] 提出了基于深度学习的神经网络架构3DRIMR 基于稀疏的原始毫米波雷达数据，以密集的点云形式
重建物体的三维形状Huang等人[20] 提出了基于密度的DBSCAN算法和Faster R-CNN网络 实现了停车场场景下的车辆检测，平均检测精度达96.95% Dreher等人[21] 对比基于网格和基于点云的目标检测方法 基于网格的方法效率更高，但在密集城市交通情况的条件下，
基于点云的网络实现了更好的检测性能Xu等人[22] 提出了RPFA-Net 显著提高网络回归航向角估计和三维目标检测的精度
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表 3 基于数据级融合方法实现成像处理相关论文汇总
Table 3. Summary of image processing papers based on data-level fusion method
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表 4 基于特征级融合方法实现成像处理相关论文汇总
Table 4. Summary of image processing papers based on feature-level fusion method
作者 网络结构 效果 Cui等人[38] 基于交叉融合策略的CNN 相较于Astyx数据集上使用的模型，该模型平均每帧提供多达1500个雷达检测点 Nabati等人[39] CenterFusion 在NuScenes数据集上进行验证，优于最先进的基于相机的目标检测方法 John等人[40] SO-Net 在NuScenes数据集上进行验证, 提高了车辆检测和语义分割的准确性，同时减少了计算时间 Chang等人[41] SAF-FCOS 所提出的SAF-FCOS在所有尺度上都比FCOS具有更好的检测性能 Bansal等人[42] RadSegNet 在密集的车辆以及恶劣的天气和照明条件下实现精确的目标检测 Lo等人[43] RCDPT 所提出的方法相较于常用的融合策略产生了更好的性能 Wu等人[44] MVFusion 在NuScenes数据集上进行验证，提高了单帧雷达-相机融合的性能
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表 5 基于决策级融合方法实现成像处理相关论文汇总
Table 5. Summary of image processing papers based on decision level-fusion method
作者 特点 效果 Sengupta等人[45] 使用匈牙利算法进行数据关联，三卡尔曼滤波方法进行跟踪 实现了鲁棒的三维目标跟踪 Zhou等人[46] 设计了自适应数据关联方法 相较于单传感器，实现了更好的跟踪性能 Bai等人[47] 提出了一种鲁棒的DATMO算法 基于GM-PHD滤波器和信息融合，解决了物体遮挡、
测量丢失等跟踪问题Jha等人[36] 使用变换矩阵融合相机与雷达数据 较为准确地实现了目标的检测和识别 Sengupta等人[48] 提出了一种相机辅助的自动雷达PCL标记和数据集生成方法 实现目标检测和识别，方法简单有效 Dong等人[49] 提出AssociationNet 计算和最小化来自点云和图像边界框之间的欧氏距离，
提高了关联的准确性
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表 6 车载毫米波SAR基础成像算法总结
Table 6. Summary of imaging algorithms for automotive millimeter wave SAR
基础成像算法 作者 雷达载频及体制 场景特点 其他特点 距离多普勒
算法(RDA)
等频域算法Feil等人[50] 77 GHz FMCW 室内中近场(0～100 m) RDA Iqbal等人[51] 77 GHz FMCW 室外停车场(0～10 m) RDA Jiang等人[52] 77 GHz FMCW 室外停车场(0～10 m) RDA Xu等人[53] 77 GHz FMCW 室外停车场(0～10 m) 二维de-chirp处理 距离徙动算法
(RMA)Wu等人[54] 24 GHz FMCW 室内停车场(0～10 m) 无 Gumbmann等人[55] 75～100 GHz SFCW 室内小物体(0～1 m) 无 Sriharsha等人[56] 60 GHz FMCW 室外停车场(0～10 m) MIMO-SAR系统 Zhang等人[57] 79 GHz FMCW 室外停车场(0～10 m) 迭代软阈值算法(ISTA) Lee等人[58] 77 GHz FMCW 室外停车场(0～10 m) 压缩感知与距离徙动算法相结合，减少数据量 Iqbal等人[59] 77 GHz FMCW 室外停车场(0～10 m) 通过子孔径数据划分的方法实现了对距离徙动算法的加速 后向投影算法(BPA) Gisder等人[61] 24 GHz FMCW 室外停车场(0～10 m) 基于GPU平台的BP算法流式处理方案 Farhadi等人[62] 77 GHz FMCW 室外中近场(0～20 m) Digital Spotlighting (DS)方法提取原始数据以降低数据量 Farhadi等人[63] 77 GHz FMCW 室外中近场(0～20 m) 动目标成像(MTI) Manzoni等人[64] 77 GHz FMCW 室外近场(0～10 m) 多径干扰抑制，MIMO-SAR系统
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表 7 车载毫米波SAR运动补偿方法总结
Table 7. Summary of motion compensation methods for automotive millimeter wave SAR
运动补偿类型 作者 具体方法 特点 额外设备测量运动参数 Feger等人[68] IMU/GNSS 工业级设备VN-300 Tagliaferri等人[69] SAR-MIN 基于低成本IMU/GNSS设备实现高精度补偿 Wu等人[70,71] 陀螺仪/加速度计 利用陀螺仪与加速度计对运动误差进行多自由度建模 多雷达协同测量运动参数 Iqbal等人[72] RANSAC 其中一部雷达通过RANSAC方法进行轨迹估计 Steiner等人[73] 运动参数估计 4部雷达协同进行3自由度的运动参数估计 参数化自聚焦 Manzoni等人[76] GCP 使用GCP方法估计BP图像的剩余多普勒相位 Manzoni等人[77] GCP 分析导航引起的轨迹误差，使用GCP方法进行雷达参数估计 Xu等人[53] 稀疏贝叶斯学习(SBL) 结合基于流域的SAR图像分割方法 非参数化自聚焦 Farhadi等人[74] 误差非空变的PGA 在BP算法中引入误差非空变的PGA自聚焦处理 Farhadi等人[75] 误差空变的PGA 在FFBP算法中引入误差空变的PGA自聚焦处理 Farhadi等人[63] PGA 应用于汽车SAR动目标成像(MTI)
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表 8 汽车毫米波雷达波形及其特点
Table 8. Summary of common automotive millimeter-wave radar waveform and its characteristics
分类 调制方法 作者 波形参数 波形特点 LFM波形改进及
参数调整频率斜坡调制 Kim等人[93] 周期10～16.5 μs 调频周期正弦变化 随机重复间隔 Kitsukawa等人[94] 带宽150 MHz 发射间隔随机改变 蝙蝠干扰回避跳频 Bechter等人[95] 载频76.3 GHz，带宽400 MHz 上移或下移频率 时间和频率分集 Hossain等人[96] 载频76.5 GHz，带宽300～500 MHz 带宽、调频率随机改变 跳频随机chirp (FHRC) Luo等人[97] 载频76.0～76.7 GHz 中心频率、带宽、调频率均随机改变 恒频与LFM组合 Yang等人[98] 载频24 GHz 恒频和斜率随机变化的三角波组合 LFM波形编码 相位编码 Uysal等人[99] 带宽0.4 GHz，16位Kasami序列编码 编码同时拉伸处理，降低采样要求 伪随机噪声(PRN)码 Liu等人[100] 载频24 GHz，20 Mbit/s 10位PRN序列调制 不同PRN码序列间相关性较低 其他波形 正交噪声波形 Xu等人[101] 载频24 GHz，带宽600 MHz 具有良好的随机相位分集 正交频分复用(OFDM) Basireddy等人[103] 载频77 GHz 以OFDM方式通过加扰的m序列产生
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表 9 汽车毫米波雷达抗互扰协调系统及策略总结
Table 9. Automotive millimeter-wave radar anti-interference coordination system and strategy summary
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表 10 汽车雷达干扰抑制的信号处理方法对比
Table 10. Comparison of signal processing methods for automotive radar interference suppression
方法大类 适用场景 优点 缺点 滤波 稳定或缓慢变化的干扰 简单，速度快，稳定干扰场景干扰抑制效果好 复杂或快速变化场景性能下降，
参考信号难获得干扰消除重构 干扰占比不高，时间上集中且特征明显 不改变未受干扰的信号部分信息得以完整保留 需要对干扰进行精确检测，
干扰占比较大时性能下降信号分离 干扰和信号在不同域中具有显著不同的特征 避免干扰的显式检测，避免信号的功率损失 计算量大，存在固有的离网问题使
分离不够准确深度学习 端到端数据处理 端到端数据处理性能出色，替代底层数据处理 缺乏理论依据，且非常依赖
输入的数据集
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表 11 不同域中的毫米波雷达干扰抑制滤波方法
Table 11. Interference suppression filtering methods for millimeter wave radar in different domains
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表 12 毫米波雷达干扰消除与重构方法总结
Table 12. Summary of interference elimination and reconstruction methods for millimeter wave radar
检测方法 作者 作用域 消除/重构方法 特点 基于阈值 Nozawa等人[116] 时域 升余弦窗 先去除相位噪声影响再在时域检测抑制干扰脉冲信号 MSER Barjenbruch等人[117] 时域 反升余弦窗 用升余弦窗口平滑，消除恢复信号的不连续性 AWEN Choi等人[118] 时域 直接消除 通过加权包络归一化算法获得更合理的阈值从而消除干扰 基于阈值 Bechter等人[119] 时域 稀疏采样IMAT 将压缩感知的稀疏采样算法应用于信号恢复，IMAT使信号恢复更准确 迭代阈值 Umehira等人[120] 时域 直接消除 基于迭代阈值检测干扰，可以检测到弱干扰 基于阈值 Neemat等人[121] 时频域 AR模型插值 在二维相关系数、振幅和相位恢复上显著改进，但不适用于高加速度目标 PELT-KCN Liu等人[122] 时域 AR模型恢复 利用已知变化数计算最优惩罚因子，比PELT算法检测准确性更高 峰值检测 Jung等人[123] 时域 卡尔曼滤波 将未失真信号部分作为卡尔曼滤波器的输出来预测失真信号部分 时域幅值 Alhumaidi等人[124] 频域 迭代频谱主峰 先在快时间和慢时间消除干扰，再迭代选择频谱主峰来近似缺失信号 CFAR Wang等人[125] 时频域 CFAR-Burg 用一维CFAR检测器在时频域中检测干扰，再用Burg算法外推 基于阈值 Wang等人[126] 时域 Matrix Pencil 将拍频信号建模为复指数之和，用MP方法估计截断信号参数 基于阈值 Rameez等人[127] 时域 慢时间AR 慢时间AR重构比快时间AR效果更好，但需要预先接收多帧数据 CFAR Yang等人[129] 时域 IAA 先用CFAR在快时间和慢时间域中检测强干扰和弱干扰，再用IAA成像
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表 13 用于毫米波雷达干扰抑制的信号分离方法总结
Table 13. Summary of signal separation methods for millimeter-wave radar interference suppression
分离方法 作者 分离基础 特点 MCA Uysal等人[130] 干扰和拍频的稀疏性 利用干扰与拍频在不同域稀疏性的差异分别做STFT和DFT后分离 IMT-EMD Wu等人[131] EMD, IMT 通过IMT算法确定EMD后的干扰主导分量并将其从信号中分离 小波去噪 Lee等人[132] 小波变换 反转小波去噪过程，提取高强度脉冲干扰，去除低强度正弦信号 Hankel矩阵分解 Wang等人[134] RPCA 将干扰信号与有用信号提升为低秩稀疏Hankel矩阵后求解RPCA问题 EMD Liu等人[135] EMD、连续均方误差 EMD分解接收信号后通过连续均方误差选择干扰分量并通过阈值抑制 TQWT Xu等人[136] 小波变换、稀疏性 利用目标信号和干扰在不同Q值小波变换中的稀疏性差异来分离干扰 GSD-IC Lee等人[137] 几何序列分解 将采样信号看作几何序列，分解成不同的非正交叠加信号，从而分离干扰 行稀疏 Wang等人[138] 干扰稀疏性和有用信号行稀疏性 利用干扰时域稀疏性和有用回波信号频域行稀疏性，引入正则化约束问题
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