高鹤婷2,,
罗丰1,,
1.西安电子科技大学雷达信号处理国家重点实验室 西安 710071
2.中国人民解放军海军701工厂 北京 100016
基金项目:国家重大科学仪器设备开发专项资金(2013YQ20060705)
详细信息
作者简介:陈世超(1992–)，女，西安电子科技大学雷达信号处理国家重点实验室博士生，研究方向为海杂波建模与仿真、海杂波背景下的目标检测。E-mail: scchen0115@163.com
高鹤婷(1981–)，女，本科毕业于哈尔滨工业大学计算机应用科学与技术专业，现就职于中国人民解放军海军701工厂，研究方向为雷达算法研究。E-mail: gaoheting_810512@sina.com
罗丰：罗 丰(1971–)，男，西安电子科技大学雷达信号处理国家重点实验室博士生导师，教授，研究方向为雷达系统设计、雷达信号与信息处理、高速实时信号处理。E-mail: luofeng@xidian.edu.cn
通讯作者:罗丰 luofeng@xidian.edu.cn
责任主编：刘宁波 Corresponding Editor: LIU Ningbo中图分类号:TN959.72
计量
文章访问数:677
HTML全文浏览量:337
PDF下载量:172
被引次数:0
出版历程
收稿日期:2020-05-30
修回日期:2020-07-30
网络出版日期:2020-08-18
Target Detection in Sea Clutter Based on Combined Characteristics of Polarization
CHEN Shichao1,,GAO Heting2,,
LUO Feng1,,
1. National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2. No.701 Factory of PLA(N), Beijing 100016, China
Funds:The National Key Scientific Instrument and Equipment Development Project Funds (2013YQ20060705)
More Information
Corresponding author:LUO Feng, luofeng@xidian.edu.cn
摘要
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。
关键词:海杂波/
目标检测/
极化特征/
一类支持向量机
Abstract: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.
Key words:Sea clutter/
Target detection/
Polarization characteristics/
One Class Support Vector Machine (OCSVM)
PDF全文下载地址:
https://plugin.sowise.cn/viewpdf/198_1c92ac31-2ec6-469f-8920-d8beb207dc21_R20072
