海杂波背景下雷达目标特征检测方法的现状与展望

许述文 白晓惠 郭子薰 水鹏朗

许述文, 白晓惠, 郭子薰, 等. 海杂波背景下雷达目标特征检测方法的现状与展望[J]. 雷达学报, 2020, 9(4): 684–714. doi: 10.12000/JR20084
引用本文: 许述文, 白晓惠, 郭子薰, 等. 海杂波背景下雷达目标特征检测方法的现状与展望[J]. 雷达学报, 2020, 9(4): 684–714. doi: 10.12000/JR20084
XU Shuwen, BAI Xiaohui, GUO Zixun, et al. Status and prospects of feature-based detection methods for floating targets on the sea surface[J]. Journal of Radars, 2020, 9(4): 684–714. doi: 10.12000/JR20084
Citation: XU Shuwen, BAI Xiaohui, GUO Zixun, et al. Status and prospects of feature-based detection methods for floating targets on the sea surface [J]. Journal of Radars, 2020, 9(4): 684–714. doi: 10.12000/JR20084

海杂波背景下雷达目标特征检测方法的现状与展望

doi: 10.12000/JR20084
基金项目: 国家自然科学基金(61871303),电波环境特性及模化技术重点实验室基金(6142403180204),111引智计划(B18039)
详细信息
    作者简介:

    许述文(1985–),男,安徽黄山人,博士,副教授,博士生导师,加拿大 Mcmaster 大学访问学者,入选陕西省青年人才托举计划。2011 年在西安电子科技大学获得博士学位,现就职于西安电子科技大学电子工程学院雷达信号处理国家重点实验室。主要研究方向为雷达目标检测、机器学习、时频分析和 SAR 图像处理。E-mail: swxu@mail.xidian.edu.cn

    白晓惠(1998–),女,陕西宝鸡人,西安电子科技大学博士生。主要研究方向为雷达目标检测、机器学习和海杂波信号处理。E-mail: xhbai@stu.xidian.edu.cn

    郭子薰(1994–),女,陕西西安人,西安电子科技大学博士生。主要研究方向为雷达目标检测、机器学习和海杂波信号处理。E-mail: zxguo_724@stu.xidian.edu.cn

    水鹏朗(1967–),男,陕西西安人,博士,教授。1999年在西安电子科技大学获得博士学位,现担任西安电子科技大学电子工程学院雷达信号处理国家重点实验室教授、硕导、博导。主要研究方向为海杂波建模、雷达目标检测和图像处理。E-mail: plshui@xidian.edu.cn

    通讯作者:

    许述文 swxu@mail.xidian.edu.cn

  • 责任主编:关键 Corresponding Editor: GUAN Jian
  • 中图分类号: TN95

Status and Prospects of Feature-based Detection Methods for Floating Targets on the Sea Surface (in English)

Funds: The National Natural Science Foundation of China (61871303), The Foundation of National Key Laboratory of Electromagnetic Environment (6142403180204), The Foreign Scholars in University Research and Teaching Programs (the 111 Project) (B18039)
More Information
    Author Bio:

    XU Shuwen was born in Huangshan city in Anhui, China. He received his B.Eng. and Ph.D. degrees, both in electronic engineering, from Xidian University, Xi’an, China, in 2006 and 2011, respectively. Subsequently, he worked at the National Laboratory of Radar Signal Processing, Xidian University. He worked as a visiting professor in McMaster University, Canada in 2018. He is currently a professor with the National Laboratory of Radar Signal Processing, Xidian University. His research interests are in the fields of radar target detection, statistical learning, and SAR image processing. E-mail: swxu@mail.xidian.edu.cn

    BAI Xiaohui was born in Baoji, Shaanxi province in 1998. She is now a Ph.D. student in Xidian University. Her main research fields are radar target detection, machine learning, and sea clutter signal processing. E-mail: xhbai@stu.xidian.edu.cn

    GUO Zixun was born in Xi’an, Shaanxi province in 1994. She is now a Ph.D. student in Xidian University. Her main research fields are radar target detection, machine learning, and sea clutter signal processing. E-mail: zxguo_724@stu.xidian.edu.cn

    SHUI Penglang was born in Xi’an, Shaanxi province in 1967. He received his Ph.D. degree in electronic engineering from Xidian University, Xi’an, China, in 1999. He is now a professor, PhD supervisor at the Radar Signal Processing National Key Lab of Electronic Engineering from Xidian University. His main research fields are sea clutter modeling, radar target detection, and image processing. E-mail: plshui@xidian.edu.cn

    Corresponding author: XU Shuwen, swxu@mail.xidian.edu.cn
  • 摘要: 海杂波背景下的雷达目标检测对民用和军事都有着重要的意义。随着海面目标的小型化和隐身化,海面慢速、漂浮小目标已经成为了雷达警戒的重点对象。关于此类小目标的检测一直以来都是海杂波背景下目标检测中的难题。通常,漂浮小目标的雷达散射横截面积(RCS)微弱,并且运动速度慢,常常在时域和频域均存在“超杂波检测”的困难。传统目标检测方法对漂浮小目标的检测存在明显的性能瓶颈。对于海面漂浮小目标的检测,采用高多普勒和高距离分辨体制(“双高”体制)是从雷达体制上解决这个问题的有效途径。在双高体制下,雷达接收的目标回波提供了更多的可用信息。然而,如何将这些更加精细化的信息转化为探测性能的提升,一直以来都是雷达届关注的难点,相关科研成果也一直在不断地推陈出新。近些年,在双高雷达体制下,学者们提出了多种基于特征的目标检测方法,作为对海智能检测的人工特征工程阶段,这些方法缓解了仅依靠能量信息较难检测小目标的困难局面,极大程度地改善了对漂浮小目标的检测性能。为了更好地让相关雷达从业者了解该领域这些年的发展和未来的趋势,该文首先总结了对海检测的难点和常用的目标检测方法,然后分析了特征检测的原理和通用框架以及国内外几种典型的基于特征的检测方法,最后对特征检测方法发展趋势进行了展望。

     

  • 图  1  常见的海面小目标

    Figure  1.  Some common small targets on the sea

    图  2  实测数据功率图及幅度拟合结果

    Figure  2.  Power map of measured data and amplitude fitting results

    图  3  自适应检测方法流程图

    Figure  3.  The flowchart of the adaptive detection methods

    图  4  VV极化、逆风情况下,海况4级时各种不同舰船的信杂比

    Figure  4.  In the case of headwind situation, SCR for various ships at sea state 4 (VV polarization)

    图  5  可实现“双高”体制的雷达工作模式

    Figure  5.  Radar working modes that realize the "double high" system

    图  6  特征检测流程图

    Figure  6.  The flowchart of the feature-based detection methods

    图  7  部署现场平面图[11]

    Figure  7.  A plan overview of the deployment site[11]

    图  8  2006年试验架设位置(OTB)[11]

    Figure  8.  Location of the deployment site in 2006 (OTB)[11]

    图  9  试验合作船只[11]

    Figure  9.  Experimental cooperative boats[11]

    图  10  X波段固态功放监视/导航雷达[51]

    Figure  10.  X-band solid-state power amplifier surveillance/navigation radar[51]

    图  11  组合脉冲发射的3种模式[51]

    Figure  11.  Three modes of combined pulse transmission[51]

    图  12  14个距离单元分形特性分析[30]

    Figure  12.  Analysis of fractal characteristics in 14 range cells[30]

    图  13  14个距离单元H(q)趋势图[30]

    Figure  13.  The trends of H(q) in 14 range cells[30]

    图  14  HH极化下纯杂波单元与含目标单元Hurst频率分布图[30]

    Figure  14.  Hurst frequency distribution of clutter cells and target cells under HH polarization[30]

    图  15  分形特征检测器的原理框图[73]

    Figure  15.  The flowchart of fractal-based detector[73]

    图  16  分形曲线[73]

    Figure  16.  Fractal curves[73]

    图  17  训练样本和待分类样本在二维特征平面的显示图[73]

    Figure  17.  The distribution of training samples and test samples on the two-dimensional feature plane[73]

    图  18  经典CFAR算法检测曲线[73]

    Figure  18.  Classic CFAR algorithm detection curves[73]

    图  19  基于神经网络预测的检测器框图[33]

    Figure  19.  The flowchart of detector based on neural network prediction[33]

    图  20  基于神经网络检测器和传统多普勒CFAR检测器的ROC曲线[33]

    Figure  20.  ROC curves of the detector based on neural network and traditional Doppler CFAR detector[33]

    图  21  基于预测的检测方法流程图

    Figure  21.  The flowchart of detection methods based on prediction

    图  22  海杂波抑制后的微动信号变换域特征(N=256)[41]

    Figure  22.  Micro-motion signal features after sea clutter suppression (N=256)[41]

    图  23  海杂波抑制后基于STFT和GSTFRFT的微动信号检测结果比较(N=512)[41]

    Figure  23.  Comparison of micro-motion target detection results based on STFT and GSTFRFT after sea clutter suppression (N=512)[41]

    图  24  基于ST-SFT的海上微动目标检测结果(起始时间=20 s)[42]

    Figure  24.  ST-SFT-based micro-motion targets detection results (starting time=20 s)[42]

    图  25  基于ST-SFRFT的海上微动目标检测结果(起始时间=20 s)[42]

    Figure  25.  ST-SFRFT-based micro-motion targets detection results (starting time=20 s)[42]

    图  26  基于CNN的检测方法流程图[43]

    Figure  26.  Processing flow diagram of method based on CNN[43]

    图  27  散斑的平均一致性因子检测器的流程图[74]

    Figure  27.  The flowchart of a feature-based detector using the average consistency factor of speckle[74]

    图  28  4种极化下纯杂波和目标杂波的平均一致性因子[74]

    Figure  28.  The average consistency factors of pure clutter and clutter with target under 4 polarization channels[74]

    图  29  L=1024, 4种极化下4种检测器的检测概率[74]

    Figure  29.  L = 1024, the detection probabilities of the four detectors under 4 polarization channels[74]

    图  30  纯杂波与目标在特征空间中的分布情况

    Figure  30.  Distributions of features of clutter-only vectors and vectors with target in 3D feature space

    图  31  原始数据生成的凸包和给定虚警率的凸包

    Figure  31.  Convex hull with the original training data and convex hull with given false alarm rate

    图  32  基于决策树的检测器流程图[78]

    Figure  32.  The flowchart of the decision-tree-based detector[78]

    图  33  极化3特征检测方法与原始3特征检测方法检测概率柱状图[76]

    Figure  33.  Detection probabilities of polarization features-based detector and tri-detector at HH, VV, HV, and VH polarizations for ten data sets[76]

    图  1  Some common small targets on sea

    图  2  Power map of measured data and amplitude fitting results

    图  3  Flowchart of adaptive detection methods

    图  4  SCRs for various ships at sea state 4 (VV polarization), in the case of headwind

    图  5  Radar working modes that realize the “double-high” system

    图  6  Flowchart of the feature-based detection methods

    图  7  A plan overview of the deployment site[11]

    图  8  Location of the deployment site in 2006 (OTB)[11]

    图  9  Experimental cooperative boats[11]

    图  10  X-band solid-state power amplifier surveillance/navigation radar[51]

    图  11  Three modes of combined pulse transmission[51]

    图  12  Analysis of fractal characteristics in 14 range cells[30]

    图  13  Trends of H(q) in 14 range cells[30]

    图  14  Hurst frequency distribution of clutter cells and target cells under HH polarization[30]

    图  15  Flowchart of fractal-based detector[73]

    图  16  Fractal curves[73]

    图  17  Distribution of training samples and test samples on the two-dimensional feature plane[73]

    图  18  Classic CFAR algorithm detection curves[73]

    图  19  Flowchart of detector based on neural network prediction[33]

    图  20  ROC curves of neural network-based detector and traditional Doppler CFAR detector[33]

    图  21  Flowchart of detection methods based on prediction

    图  22  Micro-motion signal features after sea clutter suppression (N = 256)[41]

    图  23  Comparison of micro-motion target detection results based on STFT and GSTFRFT after sea clutter suppression (N = 512)[41]

    图  24  ST-SFT-based micro-motion target detection results (starting time = 20 s)[42]

    图  25  ST-SFRFT-based micro-motion targets detection results (starting time = 20 s)[42]

    图  26  Processing flow diagram of CNN-based method[43]

    图  27  Flowchart of a feature-based detector using the average speckle consistency factor[74]

    图  28  Average consistency factors of pure clutter and clutter with target under four polarization channels[74]

    图  29  The detection probabilities of the four detectors under four polarization channels, L = 1024[74]

    图  30  Distributions of features of clutter-only vectors and vectors with target in 3D feature space

    图  31  Convex hull with the original training data and convex hull with given false alarm rate

    图  32  Flowchart of the decision tree-based detector[78]

    图  33  Detection probabilities of polarization feature-based detector and tri-detector at HH, VV, HV, and VH polarizations for 10 datasets[76]

    表  1  1993年IPIX雷达数据说明[28,50]

    Table  1.   The description of IPIX radar data collected in 1993[28,50]

    数据名称Data name浪高(m) Wave heights (m)风速(km/h) Wind speed (km/h)目标所在单元Primary受影响单元Secondary
    #172.2998, 10, 11
    #261.1976, 8
    #300.91976, 8
    #310.91976, 8, 9
    #401.0975, 6, 8
    #540.72087, 9, 10
    #2801.61087, 10
    #3100.93376, 8, 9
    #3110.93376, 8, 9
    #3200.92876, 8, 9
    下载: 导出CSV

    表  2  1998年IPIX雷达数据说明[28,50]

    Table  2.   The description of IPIX radar data collected in 1998[28,50]

    数据名称Data name距离范围(m) Range(m)目标所在单元Primary受影响单元Secondary雷达照射方向Radar direction
    #2022253201~40112423, 25
    #2025253201~401176, 8
    下载: 导出CSV

    表  3  OTB MS3的主要特性[11]

    Table  3.   Main characteristics of OTB MS3[11]

    参数Parameter数值Value
    纬度Latitude34°36'55.32"S
    经度Longitude20°17'20.11"E
    地面高度Ground height53 m
    天线高度Antenna height56 m
    离海距离Distance to sea1.2 km
    方位角范围Azimuth coverage208°~80° N (SSW-ENE)
    距离Range(CNR > 15 dB) 1.25~4.50 km
    擦地角Grazing angle(< 15 km) 3.00°~0.16°
    擦地角Grazing angle(CNR > 15 dB) 3.0°~0.7°
    下载: 导出CSV

    表  4  Fynmeet系统和性能参数[11]

    Table  4.   Fynmeet system and performance specifications[11]

    系统组成System composition系统参数System parameters参数设置Parameter values
    发射机Transmitter频率范围Frequency range6.5~17.5 GHz
    峰值功率Peak power2 kW
    PRF范围PRF range0~30 kHz
    波形Waveforms固定频率波形,步进频率波形,频率捷变波形Fixed frequency waveform, step frequency waveform, frequency agility waveform
    捷变带宽Agile bandwidth脉冲间500 MHz 500 MHz pulse to pulse
    天线Antenna类型Type双偏移反射器Dual-offset reflector
    增益Gain≥ 30 dB
    波束宽度Beamwidth≤ 2° (3 dB波束宽度) ≤2° (3 dB beamwidth)
    旁瓣Slidelobes≤ –25 dB
    接收机Receiver动态范围Dynamic range60 dB (瞬时) / 120 dB (总计) 60 dB (instantaneous)/120 dB (total)
    灵敏度Sensitivity0.1 m2 @ 10 km
    仪表范围Instrumented range200 m~15 km
    距离门Range gates1~64; ΔR = 100 ns, 300 ns or 400 ns
    采样器类型Sampler type中频采样器(IFS) Intermediate frequency sampler
    编码类型Encoding type正交编码Quadrature
    镜像抑制Image rejection≤ –41 dBc
    下载: 导出CSV

    表  5  X波段试验雷达参数[51]

    Table  5.   X-band radar parameters[51]

    雷达参数Radar parameters参数设置Parameters setting
    工作频段Working bandX
    工作频率范围Frequency range9.3~9.5 GHz
    量程Measuring range0.0625~96 nm
    扫描带宽Scanning bandwidth25 MHz
    距离分辨率Range resolution6 m
    脉冲重复频率Pulse repetition frequency1.6 K, 3 K, 5 K和10 K
    发射峰值功率Transmit peak power50 W
    天线转速Rotating speed of antenna2 rpm, 12 rpm, 24 rpm, 48 rpm
    天线长度Length of antenna1.8 m
    天线工作模式Antenna operation mode凝视、圆周扫描Gaze, circular scanning
    天线极化方式Antenna polarizationHH
    天线水平波束宽度Antenna horizontal beam width1.2°
    天线垂直波束宽度Antenna vertical beam width22°
    下载: 导出CSV

    表  6  现有特征检测方法的特征

    Table  6.   Features introduction of feature-based detection method

    现有特征Existing features
    分形特征Fractal features单分形特征[29,31,67]、多重分形特征[30,68]、分数阶傅里叶变换域的分形特征[69-73] Single fractal features[29,31,67], multifractal features[30,68], fractal features in FRFT[69-73]
    海杂波混沌特征Chaotic characteristics of sea clutter关联维、Lyapunov指数以及Kolmogorov熵 Correlation dimension, Lyapunov exponent, and Kolmogorov entropy[33][33]
    时域特征Features in the time domain相对平均幅度[28]、时域的信息熵[77]、时域的Hurst指数[77]、散斑一致性因子特征[74] Relative average amplitude[28], temporal information entropy[77], temporal Hurst exponent[77], the speckle consistency factor[74]
    频域特征Features in the frequency domain相对多普勒峰高[28]、相对多普勒谱熵[28]、频谱峰值与均值之比[77]、频域Hurst指数[78] Relative Doppler peak height[28], relative vector-entropy[28], frequency peak-to-average ratio[77], Hurst exponent in frequency domain[78]
    时频域特征Features in the time and frequency domains微多普勒特征[41,42]、归一化时频分布的时频累积[75]、由归一化时频分布亮像素构成二值图像中的连通区域数目和最大连通区域的尺寸[75] Micro-Doppler features[41,42], the ridge integration of NTFD[75], the number of connected regions and the maximum size of connected regions in a binary image[75]
    极化特征Polarization features相对体散射机制对应能量[76]、相对二面角散射机制对应能量[76]和相对面散射机制对应能量[76] Relative surface scattering power[76], relative dihedral scattering power[76], and the relative volume scattering power[76]
    下载: 导出CSV

    表  7  不同方法海上微动目标检测性能对比[42]

    Table  7.   Detection performance of different methods of micro-motion model of maritime targets[42]

    参数ParameterMTDFRFTWVDSPWVDST-SFTST-SFRFT
    Pd(%)(SCR=–5 dB)39.2657.2635.6855.2449.2171.35
    Pd(%)(SCR=0 dB)52.8476.8462.2772.5863.2885.69
    下载: 导出CSV

    表  8  不同模型目标检测结果(%)[43]

    Table  8.   The detection results of different models(%)[43]

    模型ModelLeNetAlexNetGoogLeNet
    虚警概率False alarm ratio1.240.040.24
    检测概率Detection probability92.2884.4490.94
    下载: 导出CSV

    表  9  基于SVM的检测器与其余检测器的性能对比[77]

    Table  9.   Detection performance comparisons of SVM-based detector and the other detectors[77]

    检测器Detectors检测结果(HH极化,虚警概率为0.001) Detection results (HH polarization, PF = 0.001)
    SCR=–2 dBSCR=17 dB
    基于SVM的检测器SVM-based detector7699
    基于3特征的检测器Tri-feature-based detector5799
    基于分形的检测器Fractal-based detector1879
    下载: 导出CSV

    表  10  基于决策树的检测结果和其余检测器的性能对比[78]

    Table  10.   Detection performance comparisons of the decision tree-based detector and the other detectors[78]

    检测器Detector检测结果Detection
    0 dB5 dB10 dB15 dB
    基于决策树的检测器Decision tree-based detector0.760.840.980.99
    基于3特征的检测器Tri-feature-based detector0.580.650.820.95
    基于分形的检测器Fractal-based detector0.210.320.480.68
    下载: 导出CSV

    表  1  Description of IPIX radar data collected in 1993[28,50]

    Data name Wave heights
    (m)
    Wind speed
    (km/h)
    Primary Secondary
    #17 2.2 9 9 8, 10, 11
    #26 1.1 9 7 6, 8
    #30 0.9 19 7 6, 8
    #31 0.9 19 7 6, 8, 9
    #40 1.0 9 7 5, 6, 8
    #54 0.7 20 8 7, 9, 10
    #280 1.6 10 8 7, 10
    #310 0.9 33 7 6, 8, 9
    #311 0.9 33 7 6, 8, 9
    #320 0.9 28 7 6, 8, 9
    下载: 导出CSV

    表  2  Description of IPIX radar data collected in 1998[28,50]

    Data name Range(m) Primary Secondary Radar direction
    #202225 3201~4011 24 23, 25
    #202525 3201~4011 7 6, 8
    下载: 导出CSV

    表  3  Main characteristics of OTB MS3[11]

    Parameter Value
    Latitude 34°36'55.32"S
    Longitude 20°17'20.11"E
    Ground height 53 m
    Antenna height 56 m
    Distance to sea 1.2 km
    Azimuth coverage 208°~80° N (SSW-ENE)
    Range (CNR > 15 dB) 1.25~4.50 km
    Grazing angle (<15 km) 3.00°~0.16°
    Grazing angle (CNR > 15 dB) 3.0°~0.7°
    下载: 导出CSV

    表  4  Fynmeet system and performance specifications[11]

    System composition System parameters Parameter values
    Transmitter Frequency range 6.5~17.5 GHz
    Peak power 2 kW
    PRF range 0~30 kHz
    Waveforms Fixed frequency waveform, step frequency waveform, frequency agility waveform
    Agile bandwidth 500 MHz pulse to pulse
    Antenna Type Dual-offset reflector
    Gain ≥30 dB
    Beamwidth ≤2° (3 dB beamwidth)
    Slidelobes ≤–25 dB
    Receiver Dynamic range 60 dB (instantaneous)/120 dB (total)
    Sensitivity 0.1 m2 @ 10 km
    Instrumented range 200 m~15 km
    Range gates 1~64; ΔR = 100 ns, 300 ns or 400 ns
    Sampler type Intermediate frequency sampler
    Encoding type Quadrature
    Image rejection ≤–41 dBc
    下载: 导出CSV

    表  5  X-band radar parameters[51]

    Radar parameters Parameters setting
    Working band X
    Frequency range 9.3~9.5 GHz
    Measuring range 0.0625~96 nm
    Scanning bandwidth 25 MHz
    Range resolution 6 m
    Pulse repetition frequency 1.6 K, 3 K, 5 K和10 K
    Transmit peak power 50 W
    Rotating speed of antenna 2 rpm, 12 rpm, 24 rpm, 48 rpm
    Length of antenna 1.8 m
    Antenna operation mode Gaze, circular scanning
    Antenna polarization HH
    Antenna horizontal beam width 1.2°
    Antenna vertical beam width 22°
    Information about the dataset and links to its download are available on the website of the Journal of Radar.
    下载: 导出CSV

    表  6  Features introduction of feature-based detection method

    Existing features
    Fractal features Single fractal features[29,31,67], multifractal features[30,68], fractal features in FRFT[69-73]
    Chaotic characteristics of sea clutter Correlation dimension, Lyapunov exponent, and Kolmogorov entropy[33]
    Features in the time domain Relative average amplitude[28], temporal information entropy[77], temporal Hurst exponent[77], the speckle consistency factor[74]
    Features in the frequency domain Relative Doppler peak height[28], relative vector-entropy[28], frequency peak-to-average ratio[77], Hurst exponent in frequency domain[78]
    Features in the time and frequency domains Micro-Doppler features[41,42], the ridge integration of NTFD[75], the number of connected regions and the maximum size of connected regions in a binary image[75]
    Polarization features Relative surface scattering power[76], relative dihedral scattering power[76], and the relative volume scattering power[76]
    下载: 导出CSV

    表  7  Detection performances of different methods for the detection of micro-motion maritime targets[42]

    Parameter MTD FRFT WVD SPWVD ST-SFT ST-SFRFT
    Pd(%) (SCR = –5 dB) 39.26 57.26 35.68 55.24 49.21 71.35
    Pd(%) (SCR = 0 dB) 52.84 76.84 62.27 72.58 63.28 85.69
    下载: 导出CSV

    表  8  Detection results of different models(%)[43]

    Model LeNet AlexNet GoogLeNet
    False alarm ratio 1.24 0.04 0.24
    Detection probability 92.28 84.44 90.94
    下载: 导出CSV

    表  9  Detection performance comparison between SVM-based detector and other detectors[77]

    Detectors Detection results
    (HH polarization, PF = 0.001)
    SCR = –2 dB SCR=17 dB
    SVM-based detector 76 99
    Tri-feature-based detector 57 99
    Fractal-based detector 18 79
    下载: 导出CSV

    表  10  Detection performance comparisons between the decision tree-based detector and the other detectors[78]

    Detector Detection results
    0 dB 5 dB 10 dB 15 dB
    Decision tree-based detector 0.76 0.84 0.98 0.99
    Tri-feature-based detector 0.58 0.65 0.82 0.95
    Fractal-based detector 0.21 0.32 0.48 0.68
    下载: 导出CSV
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  • 收稿日期:  2020-06-25
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