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Status and Prospects of Feature-based Detection Methods for Floating Targets on the Sea Surface (in English)
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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。Abstract: Radar target detection in sea clutter is of significance to both the civil and military applications. With the miniaturization and invisibility of sea targets, Small Floating Targets (SFTs) with slow speed have become the focus of radar detection. However, the detection of SFTs in the background of sea clutter has always been a challenging problem. SFTs usually have a weak Radar Cross Section (RCS) and slow speed, making them difficult to be detected in sea clutter. Traditional target detection methods exhibit poor performance in the detection of SFTs. For the detection of small and weak targets on the sea surface, a high Doppler resolution and high range resolution system (double-high system) is an effective approach to solve this problem. In the double-high system, the target echo received by the radar provides readily available and sufficient information. However, how to transform and refine this information to improve detection performance has always been a challenge to the radar industry. In recent years, as an artificial feature engineering stage for intelligent radar target detection, scholars have proposed various feature-based target detection methods based on the double-high system to alleviate the difficulty of SFT detection when relying only on energy information and to considerably improve the detection performance. To ensure that relevant radar practitioners better understand the development of this field in recent years and the future trend, this paper summarizes the difficulties of sea target detection and common target detection methods, analyzes the principle and general framework of feature detection and several typical feature-based detection methods, and explores the development trend of feature-based detection methods.
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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 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 14 Hurst frequency distribution of clutter cells and target cells under HH polarization[30]
图 17 Distribution of training samples and test samples on the two-dimensional feature plane[73]
图 20 ROC curves of neural network-based detector and traditional Doppler CFAR detector[33]
图 23 Comparison of micro-motion target detection results based on STFT and GSTFRFT after sea clutter suppression (N = 512)[41]
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 33 Detection probabilities of polarization feature-based detector and tri-detector at HH, VV, HV, and VH polarizations for 10 datasets[76]
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 #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 
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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 #202225 3201～4011 24 23, 25 
#202525 3201～4011 7 6, 8 
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参数Parameter 数值Value 纬度Latitude 34°36'55.32"S 经度Longitude 20°17'20.11"E 地面高度Ground height 53 m 天线高度Antenna height 56 m 离海距离Distance to sea 1.2 km 方位角范围Azimuth coverage 208°～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°
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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 range 6.5～17.5 GHz 峰值功率Peak power 2 kW PRF范围PRF range 0～30 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 range 60 dB (瞬时) / 120 dB (总计) 60 dB (instantaneous)/120 dB (total) 灵敏度Sensitivity 0.1 m2 @ 10 km 仪表范围Instrumented range 200 m～15 km 距离门Range gates 1～64; 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
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雷达参数Radar parameters 参数设置Parameters setting 工作频段Working band X 工作频率范围Frequency range 9.3～9.5 GHz 量程Measuring range 0.0625～96 nm 扫描带宽Scanning bandwidth 25 MHz 距离分辨率Range resolution 6 m 脉冲重复频率Pulse repetition frequency 1.6 K, 3 K, 5 K和10 K 发射峰值功率Transmit peak power 50 W 天线转速Rotating speed of antenna 2 rpm, 12 rpm, 24 rpm, 48 rpm 天线长度Length of antenna 1.8 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 polarization HH 天线水平波束宽度Antenna horizontal beam width 1.2° 天线垂直波束宽度Antenna vertical beam width 22°
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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]
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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
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模型Model LeNet AlexNet GoogLeNet 虚警概率False alarm ratio 1.24 0.04 0.24 检测概率Detection probability 92.28 84.44 90.94
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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 dB SCR=17 dB 基于SVM的检测器SVM-based detector 76 99 基于3特征的检测器Tri-feature-based detector 57 99 基于分形的检测器Fractal-based detector 18 79
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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 dB 5 dB 10 dB 15 dB 基于决策树的检测器Decision tree-based detector 0.76 0.84 0.98 0.99 基于3特征的检测器Tri-feature-based detector 0.58 0.65 0.82 0.95 基于分形的检测器Fractal-based detector 0.21 0.32 0.48 0.68
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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
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Data name Range(m) Primary Secondary Radar direction #202225 3201～4011 24 23, 25
#202525 3201～4011 7 6, 8
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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°
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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
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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.
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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]
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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
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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
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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
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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
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