| 基于电磁散射特征参数提取与数据分布分析的非线性目标识别 |
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| 引用本文： | 张量,刘洋,王飞,王天庭,吴先良.基于电磁散射特征参数提取与数据分布分析的非线性目标识别[J].电波科学学报,2019,34(1):52-59. |
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| 作者姓名： | 张量 刘洋 王飞 王天庭 吴先良 |
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| 作者单位： | 安徽大学 教育部智能计算与信号处理重点实验室, 合肥 230601;合肥师范学院 电子信息系统仿真设计安徽省重点实验室, 合肥 230601;安徽大学 教育部智能计算与信号处理重点实验室,合肥,230601 |
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| 基金项目： | 国家自然科学基金61701163国家自然科学基金514770392018年度安徽省自然科学研究项目重大项目KJ2018ZD046 |
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| 摘 要： | 非线性目标的探测与识别在国防、反恐、安保、救援、交通安全等领域均具有重要意义.为提高上述场景下对非线性目标的探测识别能力,文中以典型的非线性器件——肖特基二极管为例,构建了非线性目标的谐波散射模型,并在此基础上利用不同类型非线性目标散射的各次谐波强度与变化趋势不同的特性,提出了一种利用数理方法提取分析目标散射特征参数进而实现对非线性目标探测识别的方法.实验结果显示,本次提出的方法在小样本下对未知非线性目标的识别率在81%左右,证明了该方法对非线性器件有较好的探测与识别能力.
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| 关 键 词： | 电磁散射 特征提取 目标识别 非线性目标 数据分布 |
| 收稿时间： | 2018-08-13 |
Nonlinear target recognition based on electromagnetic scattering feature parameter extraction and data distribution analysis |
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| ZHANG Liang,LIU Yang,WANG Fei,WANG Tianting,WU Xianliang.Nonlinear target recognition based on electromagnetic scattering feature parameter extraction and data distribution analysis[J].Chinese Journal of Radio Science,2019,34(1):52-59. |
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| Authors: | ZHANG Liang LIU Yang WANG Fei WANG Tianting WU Xianliang |
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| Affiliation: | 1.Key Lab of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230601, China2.Anhui Province Key Laboratory of simulation and design for Electronic information system, Hefei Normal University, Hefei 230601, China |
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| Abstract: | The detection and identification of nonlinear targets havea great significance in the fields of national defense, anti-terrorism, security, rescue, and traffic safety. In order to improve the detection and recognition ability of nonlinear targets in the above scenarios, taking a typical nonlinear device, Schottky diode, as an example, a harmonic scattering model of nonlinear targets is constructed. On this basis, using the characteristics of different harmonic intensity and variation trend of different types of nonlinear target scattering, a method of extracting and analyzing the target scattering characteristic parameters by using mathematical methods to realize the detection and recognition of nonlinear targets is proposed. The experimental results show that the proposed method has a recognition rate of 81% for unknown nonlinear targets under small samples, which proves that the method has better detection and recognition ability for nonlinear devices. |
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| Keywords: | scattering featureextraction targetrecognition nonlineartargets data distribution |
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