汽车毫米波雷达信号处理技术综述

黄岩 张慧 兰吕鸿康 邓坤 杨阳 张睿哲 刘江 张彦君 王韵旋 周睿 徐俊 奚新锁 张侠 郑凯航 刘育铭 洪伟

黄岩, 张慧, 兰吕鸿康, 等. 汽车毫米波雷达信号处理技术综述[J]. 雷达学报, 2023, 12(5): 923–970. doi: 10.12000/JR23119
引用本文: 黄岩, 张慧, 兰吕鸿康, 等. 汽车毫米波雷达信号处理技术综述[J]. 雷达学报, 2023, 12(5): 923–970. doi: 10.12000/JR23119
HUANG Yan, ZHANG Hui, LAN Lyuhongkang, et al. Overview of signal processing techniques for automotive millimeter-wave radar[J]. Journal of Radars, 2023, 12(5): 923–970. doi: 10.12000/JR23119
Citation: HUANG Yan, ZHANG Hui, LAN Lyuhongkang, et al. Overview of signal processing techniques for automotive millimeter-wave radar[J]. Journal of Radars, 2023, 12(5): 923–970. doi: 10.12000/JR23119

汽车毫米波雷达信号处理技术综述

doi: 10.12000/JR23119
基金项目: 国家自然科学基金(62271142, 62188102, 62301152),中央高校基本科研业务费专项资金(2242022K60008)
详细信息
    作者简介:

    黄 岩,副教授,博士生导师,主要研究方向为雷达信号处理、通信感知一体化及人工智能应用技术

    张 慧,副研究员,硕士生导师,主要研究方向为汽车毫米波雷达系统及信号处理

    兰吕鸿康,硕士生,主要研究方向为汽车毫米波雷达信号处理、雷达点云成像

    邓 坤,硕士生,主要方向为汽车毫米波雷达信号处理、SAR成像

    杨 阳,硕士生,主要方向为汽车毫米波雷达信号处理、 SAR成像

    张睿哲,硕士生,主要研究方向为雷达信号处理、雷达抗干扰技术

    刘 江,博士生,主要研究方向为汽车毫米波雷达信号处理、人工智能

    张彦君,博士生,主要研究方向为汽车毫米波雷达信号处理、人工智能

    王韵旋,博士生,主要研究方向为雷达信号处理、雷达抗干扰技术

    周 睿,博士生,主要研究方向为毫米波雷达芯片设计、毫米波雷达系统设计

    徐 俊,副研究员,硕士生导师,主要研究方向为毫米波系统及天线阵列设计

    奚新锁,硕士生,主要研究方向为毫米波雷达系统设计

    张 侠,硕士生,主要研究方向为毫米波雷达系统设计

    郑凯航,硕士生,主要研究方向为汽车毫米波雷达信号处理、雷达点云成像

    刘育铭,硕士生,主要研究方向为汽车毫米波雷达信号处理、雷达点云成像、SAR成像

    洪 伟,教授,博士生导师,主要研究方向为电磁场与微波技术、毫米波亚毫米波理论与技术、无线通信射频与天线技术

    通讯作者:

    黄岩 yellowstone0636@hotmail.com

    张慧 huizhang@seu.edu.cn

  • 责任主编:张磊 Corresponding Editor: ZHANG Lei
  • 中图分类号: TN95

Overview of Signal Processing Techniques for Automotive Millimeter-wave Radar

Funds: The National Natural Science Foundation of China (62271142, 62188102, 62301152), The Fundamental Research Funds for the Central Universities (2242022K60008)
More Information
  • 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。

     

  • 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

    图  2  基于BPSK正交波形的MIMO-FMCW 4D雷达成像[2]

    Figure  2.  MIMO-FMCW 4D radar imaging based on BPSK orthogonal waveform[2]

    图  3  MilliPoint系统3D点云生成[3]

    Figure  3.  The 3D point cloud generated by MilliPoint system[3]

    图  4  文献[7]提出的PreCFAR方法与传统SO-CFAR方法生成点云数量对比

    Figure  4.  Comparison of the number of point clouds generated by PreCFAR method proposed in Ref. [7] and the traditional SO-CFAR method

    图  5  文献[8]中三维点云成像结果

    Figure  5.  3D point cloud imaging results in Ref. [8]

    图  6  RANSAC算法实现道路检测[11]

    Figure  6.  Road boundary detection based on RANSAC algorithm[11]

    图  7  RPDNet[13,14]生成的点云图像与CFAR, Lidar对比

    Figure  7.  Comparison of point cloud image generated by RPDNet[13,14] with CFAR and Lidar

    图  8  3DRIMR [18,19]以点云形式重建汽车的三维形状

    Figure  8.  3DRIMR[18,19] used to reconstruct the three-dimensional shape of the car in point cloud form

    图  9  文献[20]所提出方法检测结果

    Figure  9.  Detection results of the method proposed in Ref. [20]

    图  10  RPFA-Net[22]和PointPillars[23]在AstyxHiRes数据集中的检测结果

    Figure  10.  Test results of RPFA-Net[22] and PointPillars[23] in AstyxHiRes dataset

    图  11  文献[34]中基于AstyxHiRes数据集的三维目标检测结果

    Figure  11.  3D target detection results based on AstyxHiRes dataset in Ref. [34]

    图  12  SS方法以及RRPN[35]方法的检测结果

    Figure  12.  Detection results of SS method and RRPN[35] method

    图  13  不同场景下CenterFusion[39]在NuScenes数据集上的三维检测结果

    Figure  13.  Three-dimensional detection in different scenarios results of CenterFusion[39] on the NuScenes dataset

    图  14  MVFusion[44]在NuScenes数据集上的三维检测结果

    Figure  14.  3D detection results of MVFusion[44] on the NuScenes dataset

    图  15  文献[46]中测试的3个场景及3D跟踪结果

    Figure  15.  Three scenes tested in Ref. [46] and 3D tracking results

    图  16  RDA近场场景SAR成像验证[50,51]

    Figure  16.  The verification of RDA near-field scene SAR imaging[50,51]

    图  17  车载场景RDA成像[52]

    Figure  17.  RDA imaging for automotive scenes[52]

    图  18  停车场车位检测和识别[53]

    Figure  18.  Parking space detection and recognition[53]

    图  19  不同倾斜程度铝楔块的94 GHz毫米波SAR成像结果[55]

    Figure  19.  94 GHz millimeter-wave SAR imaging results of polyethylene cylinders with different tilting degrees[55]

    图  20  停车场SISO-RMA SAR图像[56]

    Figure  20.  Parking lots SISO-RMA SAR image[56]

    图  21  RMA与CS结合后的SAR图像[57]

    Figure  21.  SAR images using RMA & CS[57]

    图  22  改进的RMA成像[59]

    Figure  22.  Modified RMA imaging[59]

    图  23  BP算法流式处理方案试验场景[61]

    Figure  23.  The test scene of BP algorithm flow processing scheme[61]

    图  24  基于BP算法的动目标成像[63]

    Figure  24.  Moving Target Imaging (MTI) base on BP algorithm[63]

    图  25  多径干扰抑制对比图[64]

    Figure  25.  Multipath interference suppression comparison chart[64]

    图  26  不同波束角度下车载SAR的成像结果[65]

    Figure  26.  Imaging results of automotive SAR at different beam angles[65]

    图  27  自适应子孔径积累图像增强[66]

    Figure  27.  Image enhancement by adaptive sub-aperture accumulation[66]

    图  28  多车协作下提高距离向分辨率的效果比较[67]

    Figure  28.  Comparison of improving range-direction resolution in multi-vehicle collaboration[67]

    图  29  77 GHz车载SAR系统[68]

    Figure  29.  77 GHz automotive SAR system[68]

    图  30  综合运动补偿方法[69]

    Figure  30.  Comprehensive motion compensation method[69]

    图  31  多雷达协同运动补偿[72,73]

    Figure  31.  Motion compensation by multi-radar system [72,73]

    图  32  PGA运动补偿[74,75]

    Figure  32.  PGA motion compensation[74,75]

    图  33  剩余多普勒方法[76]

    Figure  33.  Residual Doppler method[76]

    图  34  MIMO-SAR运动补偿[77]

    Figure  34.  MIMO-SAR motion compensation[77]

    图  35  车载前视场景的2D分辨率[78]

    Figure  35.  2D resolution of vehicle front view scene[78]

    图  36  前视扫描SAR成像[80]

    Figure  36.  Forward scanning SAR imaging[80]

    图  37  分层MIMO-SAR成像[81]

    Figure  37.  Hierarchical MIMO-SAR imaging[81]

    图  38  多普勒复用MIMO-SAR成像[82]

    Figure  38.  Doppler multiplexing MIMO-SAR imaging[82]

    图  39  77 GHz前视MIMO-SAR系统[8587]

    Figure  39.  77 GHz forward-looking MIMO-SAR system[8587]

    图  40  前视SAR成像结果[88]

    Figure  40.  Forward-looking SAR imaging[88]

    图  41  无干扰与有强干扰的距离多普勒图像[92]

    Figure  41.  Range Doppler images without interference and with strong interference[92]

    图  42  简单的抗互扰改进FMCW波形[9395]

    Figure  42.  Simple anti-interference improved FMCW waveform[9395]

    图  43  调整多个参数的抗互扰改进FMCW波形[96,97]

    Figure  43.  FMCW waveform improved by anti-interference adjustment of multiple parameters[96,97]

    图  44  基于m-序列的OFDM雷达[103]

    Figure  44.  OFDM radar based on m-sequence[103]

    图  45  PMCW雷达框图[104]

    Figure  45.  PMCW radar block diagram[104]

    图  46  系统协调和策略抗互扰架构[105107]

    Figure  46.  System coordination and policy anti-interference architecture[105107]

    图  47  RadChat的系统组成[108]

    Figure  47.  The system composition of RadChat[108]

    图  48  采用自适应噪声抵消器的干扰抑制方案[110]

    Figure  48.  Adaptive noise canceller used to suppress interference[110]

    图  49  用于干扰抑制的自适应DBF[112,115]

    Figure  49.  Adaptive DBF for interference suppression[112,115]

    图  50  迭代阈值的时域干扰检测[120]

    Figure  50.  Iterative threshold interference detection in time domain[120]

    图  51  STFT域中拍频插值[121]

    Figure  51.  Beat interpolation in STFT domain[121]

    图  52  时频域拍频信号[125]

    Figure  52.  Beat signal in the time-frequency domain[125]

    图  53  IMIA算法[129]

    Figure  53.  IMIA algorithm[129]

    图  54  时域信号EMD[131]

    Figure  54.  Time domain signal EMD[131]

    图  55  Hankel矩阵的稀疏低秩分解[134]

    Figure  55.  Sparse low-rank decomposition of Hankel matrix[134]

    图  56  具有GRU的RNN模型[139]

    Figure  56.  RNN model with GRUs[139]

    图  57  具有自我注意机制的RNN模型[141]

    Figure  57.  RNN models with self-attention mechanisms[141]

    图  58  FCN模型的一般架构[142]

    Figure  58.  General architecture of the FCN model[142]

    图  59  基于AE的CNN模型架构[143]

    Figure  59.  CNN model architecture based on AE[143]

    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
    下载: 导出CSV

    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
    下载: 导出CSV

    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

    作者 网络结构 效果
    Meyer等人[34] Fast R-CNN 提出了AstyxHiRes数据集,实现了三维目标检测
    Nabati等人[35] RRPN 相较于Fast R-CNN,速度和准确性更高
    Jha等人[36] YOLOv3 在能见度低的环境中实现车辆导航的实时检测和识别
    Nobis等人[37] CRF-Net 检测性能优于目前最先进的图像检测网络
    下载: 导出CSV

    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
    下载: 导出CSV

    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
    下载: 导出CSV

    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)
    Gumbmann等人[55] 75~100 GHz SFCW 室内小物体(0~1 m)
    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
    下载: 导出CSV

    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)
    下载: 导出CSV

    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
    下载: 导出CSV

    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

    对象 系统/策略 作者 构成 特点
    交通系统 RadarMAC Khoury等人[105] 系统架构和雷达参数分配算法 汽车通过LTE链路连接到云中心,中心通过算法
    进行动态参数分配
    中央代理 Mazher等人[106] 通信基础设施、V2I模块资源分配策略 雷达依靠V2I通信模块与基站连接,
    基站分配FMCW雷达资源
    RadCom Aydogdu等人[107] 雷达与通信间、rMAC, cMAC方案 建立在FDM、用于雷达的TDMA和
    用于通信的CSMA组合基础上
    车辆 RadChat Aydogdu等人[108,109] 硬件单元和分布式组网协议 建立在相同的雷达与通信硬件和MAC的基础上
    下载: 导出CSV

    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
    下载: 导出CSV

    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

    方法 作者 作用域 特点
    自适应噪声抵消 Jin等人[110] 时域 将信号正频率和负频率分量分别输入自适应噪声消除器的主信道和参考信道
    FFT单元幅值非线性滤波 Wagner等人[111] 频域 对多个chirp的FFT单元幅值进行非线性滤波,保留初始相位信息
    DBF/自适应DBF Bechter等人[112,114] 空域 干扰功率在空域中可被分割为两个方向,且主要受实际孔径大小影响,消除后
    零陷将展宽;有用信号则主要受虚拟孔径影响,分离后零陷将更窄。构造
    零陷抑制干扰的前提是实际天线数量大于干扰信号数量。
    Rameez等人[115]
    Artyukhin等人[113]
    下载: 导出CSV

    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
    下载: 导出CSV

    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
    MCAUysal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-EMDWu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
    EMDLiu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
    TQWTXu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-ICLee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
    下载: 导出CSV
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  • 收稿日期:  2023-07-04
  • 修回日期:  2023-10-13
  • 网络出版日期:  2023-10-26
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