摘要
该文从全极化体制角度出发,提出一种基于极化联合特征的海面目标检测方法。首先基于极化协方差矩阵,通过Cloude特征分解,提取表征回波随机程度的极化熵和反熵的数学期望;接着直接基于极化散射矩阵,通过Krogager特征分解,提取表征回波中极化散射分量结构组成的球散射体分量、二面角散射体分量和螺旋体散射分量的归一化系数;由提取的特征构成五维特征空间,利用主成分分析(PCA)降维证明所提特征具有良好的可分性,最后采用一类支持向量机(OCSVM)对目标和杂波进行识别。所提方法分别从极化相干和非相干分解两个角度出发,通过两种不同的极化分解方式提取特征,在一定程度上解决了高海情下基于单一极化分解方法存在的检测效果不理想的问题。通过IPIX实测数据验证所提方法具有良好的检测能力。
Polarization is a property applying to transverse waves that specifies the geometrical orientation of the oscillations.This paper proposes a method for detecting small targets on the sea surface based on the combination of polarization features of two models.The scattering mechanism of sea clutter is random scattering at low glazing angle or glancing angle and the randomness is high as the angles do not have any specified shape.However,a target has a specific shape,and thus,the randomness of scattering will be less.Clutter is a term used for unwanted echoes in electronic systems,particularly in reference to radars.Such echoes typically return from ground,sea,rain,and animals/insects.In this literature,the randomness of a scattering mechanism in an echo is obtained from the probability density functions of polarization entropy using the Cloude decomposition model.Further,the proportion of scattering at spherical,dihedral,and helicoid angles from the target echoes will be different in the sea clutter.Therefore,the relative coefficient of power of these three scattering components in each echo is extracted based on Krogager polarization decomposition.Then,polarization features with good separability and complementarity are selected to form the polarization feature vector,and the characteristics are verified by Principle Component Analysis(PCA).Finally,One Class Support Vector Machine(OCSVM)is used for classification and recognition based on the polarization decomposition feature vector.Instead of single-polarization detection methods,our method uses two polarization modes to extract the decomposition features with separability and complementarity through polarization coherent decomposition and incoherent decomposition,respectively.The experimental results of the IPIX data show the effectiveness of our method.Thus,the detection performance of our model is better than those methods based on single-polarization decomposition in complex and difficult sea conditions.
作者
陈世超
高鹤婷
罗丰
CHEN Shichao;GAO Heting;LUO Feng(National Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China;No.701 Factory of PLA(N),Beijing 100016,China)
出处
《雷达学报（中英文）》
CSCD
北大核心
2020年第4期664-673,共10页
Journal of Radars
基金
国家重大科学仪器设备开发专项资金(2013YQ20060705)。
关键词
海杂波
目标检测
极化特征
一类支持向量机
Sea clutter
Target detection
Polarization characteristics
One Class Support Vector Machine(OCSVM)
