海杂波背景下的海上小目标是海洋雷达探测的重难点.针对特征空间内海杂波与小目标特征可分性问题,提出了量化特征之间可分性的度量标准——重叠系数.通过开展对海探测试验获取的2～5级海况实测数据,分别提取时域特征相对平均幅度(Relative Average Amplitude,RAA)、相对峰值峰高(Relative Peak Height,RPH)、时域熵值均值(Time domain Entropy Mean,TEM),频域特征相对多普勒峰高(Relative Doppler Peak Height,RDPH)、相对多普勒向量熵(Relative Vector Entropy,RVE)、频域熵值二阶矩(Second moment of Frequency domain Entropy,SOFE),计算出重叠系数.通过特征检测器进行检测性能对比,低海况下,相对平均幅度、相对峰值峰高、时域熵值均值、相对多普勒峰高、频域熵值二阶矩特征之间重叠系数均在0.3以下,对应特征检测器的检测概率均在85%以上;高海况下其特征之间重叠系数均在0.7以上,对应特征检测器的检测概率均在50%以下.相对多普勒向量熵在4种海况下可分性较小,其对应的特征检测器性能较差.结果验证了重叠系数在一维特征选择的应用可行性,为多特征融合目标检测提供了一定支持.
One-Dimensional Feature Selection Method Based on Radar Target Feature Divisibility
Small targets on the sea under the background of sea clutter are the key and difficult points of ocean radar de-tection.It is proposed that a measurement standard for quantifying the separability between features-overlap coefficient to response to the issue of separability between sea clutter and small target features in the feature space.By conducting sea detection experiments on measured sea conditions at levels 2～5,the relative average amplitude,relative peak height,and mean time domain entropy of time-domain features are extracted,as well as the relative Doppler peak height,relative Doppler vector entropy,and second-order moment of frequency-domain entropy of frequency-domain features,the over-lap coefficient is calculated.By comparing the detection performance through feature detectors,under low sea conditions,the overlap coefficients between the relative average amplitude,relative peak height,time-domain entropy mean,relative Doppler peak height,and frequency-domain entropy second-order moment features are all below 0.3,and the detection probability of the corresponding feature detectors is above 85%;under high sea conditions,the overlap coefficients be-tween its features are all above 0.7,and the detection probability of the corresponding feature detectors is below 50%.Relative vector entropy has low separability under four sea conditions,and its corresponding feature detectors have poor performance.The conclusion verifies the feasibility of applying overlap coefficients in one-dimensional feature selection,providing some support for multi-feature fusion target detection.
small targets at seasea clutterfeature extractionradar testingobject detection
万方数据
维普
