基于熵特征的调频引信目标与干扰信号识别

doi:10.3969/j.issn.1000-1093.2017.02.007

兵工学报 ›› 2017, Vol. 38 ›› Issue (2): 254-260.doi: 10.3969/j.issn.1000-1093.2017.02.007

基于熵特征的调频引信目标与干扰信号识别

黄莹¹，郝新红¹，孔志杰¹，张彪²

（1.北京理工大学机电动态控制重点实验室，北京 100081；2.中国科学院国家空间科学中心，北京 100190）

收稿日期:2016-07-07 修回日期:2016-07-07 上线日期:2017-04-01
作者简介:黄莹(1990—), 女, 硕士研究生。E-mail: 958732866@qq.com
基金资助:
国防“973”计划项目（613196）

Recognition of Target and Jamming Signal for FM Fuze Based on Entropy Features

HUANG Ying¹， HAO Xin-hong¹， KONG Zhi-jie¹， ZHANG Biao²

(1.Science and Technology on Electromechanical Dynamic Control Laboratory, Beijing Institute of Technology, Beijing 100081, China;2.National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China)

Received:2016-07-07 Revised:2016-07-07 Online:2017-04-01

摘要/Abstract

摘要： 针对调频引信较难抑制的调幅扫频类干扰，提出了一种基于熵特征的目标与干扰信号分类识别方法。比较目标和干扰信号作用下引信检波信号时域和频域特征，提取检波信号的香农熵和奇异谱熵，通过Kruskal-Wallis检验方法验证了特征参量的有效性，并利用支持向量机分类器对目标信号和干扰信号进行了分类识别。仿真实验结果表明：在支持向量机核函数参数最优时，该方法的分类识别正确率达到98.954%，有效提高了调频引信的抗扫频式干扰能力。

关键词: 兵器科学与技术, 调频引信, 目标识别, 扫频干扰

Abstract: A classification and recognition method of target and jamming signal based on entropy features is proposed to restrain the AM frequency-sweeping jamming of FM fuze. The time domain and frequency domain characteristics of output signal of fuze detector under the action of target and jamming signal are compared, the Shannon entropy and singular spectrum entropy of fuze detection output signal are extracted and verified to be valid by Kruskal-Wallis test method, and the support vector machine is used for classification and recognition of target signal and jamming signals. The experimental results show that the correct recognition rate of the proposed method is 98.954%, and it can effectively improve the ability of the anti-frequency sweeping jamming of FM fuze. Key

Key words: ordnancescienceandtechnology, FMfuze, targetrecognition, frequency-sweepingjamming

中图分类号:

TJ43⁺4.1

黄莹，郝新红，孔志杰，张彪. 基于熵特征的调频引信目标与干扰信号识别[J]. 兵工学报, 2017, 38(2): 254-260.

HUANG Ying， HAO Xin-hong， KONG Zhi-jie， ZHANG Biao. Recognition of Target and Jamming Signal for FM Fuze Based on Entropy Features[J]. Acta Armamentarii, 2017, 38(2): 254-260.

参考文献

［1］崔占忠, 宋世和, 徐立新. 近炸引信原理[M].第3版. 北京: 北京理工大学出版社, 2009:18-38.
CUI Zhan-zhong, SONG Shi-he, XU Li-xin. Principle of proximity fuze[M]. 3rd ed. Beijing: Beijing Institute of Technology Press, 2009:18-38.(in Chinese)
[2］刘景萍, 赵惠昌, 黄文良. 无线电引信的有源干扰机理[J]. 制导与引信, 2001, 22(4):50-52.
LIU Jing-ping, ZHAO Hui-chang, HUANG Wen-liang. Active jamming mechanism of radio fuzes[J]. Guidance & Fuze, 2001, 22(4):50-52.(in Chinese)
[3］李靖超. 基于三维熵特征的雷达信号识别[J]. 上海电机学院学报, 2015, 18(3):136-140.
LI Jing-chao. Radar signal recognition based on three dimensional entropy features[J]. Journal of Shanghai Dianji University, 2015, 18(3):136-140.(in Chinese)
[4］白航, 赵拥军, 沈伟，等. 基于时频分布Rényi熵特征的雷达辐射源识别[J]. 电路与系统学报, 2013, 18(1):437-442.
BAI Hang, ZHAO Yong-jun, SHEN Wei, et al. Radar emitter recognition based on Rényi entropy of time-frequency distribution[J]. Journal of Circuits and Systems, 2013, 18(1): 437-442.(in Chinese)
[5］谢平, 杜义浩, 黄双峰. 基于信息熵的复杂机械系统非线性特征提取方法研究[J]. 振动与冲击, 2008, 27(7):135-137.
XIE Ping, DU Yi-hao, HUANG Shuang-feng. Research on non-linear feature extracting method of complex mechanical system based on information entropy[J]. Journal of Vibration and Shock, 2008, 27(7):135-137.(in Chinese)
[6］马建华, 刘宏伟, 保铮. 固定翼飞机和直升机的分类方法研究[J]. 现代雷达, 2004, 26(12):45-48.
MA Jian-hua, LIU Hong-wei, BAO Zheng. Research on helicopter and fixed-wing aircraft classification[J]. Modern Radar, 2004, 26(12):45-48.(in Chinese)
[7］蒋正新, 施国梁. 矩阵理论及应用[M]. 北京: 北京航空学院出版社, 1988:87-95.
JIANG Zheng-xin, SHI Guo-liang. Matrix theory and application[M]. Beijing: Beijing Aeronautical Institute Press, 1988:87-95.(in Chinese)
[8］高清清, 贾民平. 基于EEMD的奇异谱熵在旋转机械故障诊断中的应用[J]. 东南大学学报: 自然科学版, 2011, 41(5):998-1001.
GAO Qing-qing, JIA Min-ping. EEMD method based singular value spectral entropy in fault diagnosis of rotating machinery[J]. Journal of Southeast University: Natural Science Edition, 2011, 41(5):998-1001.(in Chinese)
[9］陶卿, 刘欣, 唐升平，等. 基于支持向量机的弹道识别及其在雷达弹道外推中的应用[J]. 兵工学报, 2005, 26(3):308-311.
TAO Qing, LIU Xin, TANG Sheng-ping, et al.Support vector machine-based trajectory recognition with applications in the adjustment of radar trajectory prediction[J]. Acta Armamentarii, 2005, 26(3):308-311.(in Chinese)
[10］陈冰梅, 樊晓平, 周志明，等. 支持向量机原理及展望[J]. 制造业自动化, 2010, 32(12):136-138.
CHEN Bing-mei, FAN Xiao-ping, ZHOU Zhi-ming, et al. The principle and prospect of support vector machine[J]. Manufacturing Automation, 2010, 32(12): 136-138.(in Chinese)
[11］李坤, 刘鹏, 吕雅洁，等. 基于Spark的LIBSVM参数优选并行化算法[J]. 南京大学学报:自然科学, 2016, 52(2):343-352.
LI Kun, LIU Peng, LYU Ya-jie, et al. The parallel algorithms for LIBSVM parameter optimization based on Spark[J]. Journal of Nanjing University:Natural Sciences,2016, 52(2):343-352. (in Chinese)
[12］陈慧玲. 调频连续波无线电引信目标与干扰信号识别方法[D]. 北京: 北京理工大学, 2015.
CHEN Hui-ling. On recognition of target and interference signal for FM-CM radio proximity fuze[D]. Beijing: Beijing Institute of Technology, 2015.(in Chinese)

第38卷
第2期2017 年2月兵工学报ACTA
ARMAMENTARIIVol.38No.2Feb.2017

[1]	宋晓茹, 刘康, 高嵩, 陈超波, 阎坤. 复杂战场环境下改进YOLOv5军事目标识别算法研究[J]. 兵工学报, 2024, 45(3): 934-947.
[2]	赵晓冬, 张洵颖. 基于YOLOv5的无人车自主目标识别优化算法[J]. 兵工学报, 2023, 44(9): 2732-2744.
[3]	刘冰, 郝新红, 周文, 杨瑾. 基于BAS-BPNN的调频无线电引信目标与扫频干扰识别方法[J]. 兵工学报, 2023, 44(8): 2391-2403.
[4]	周文, 郝新红, 董二娃, 陈彦君. 基于滑动多周期FFT的调频引信抗扫频干扰方法[J]. 兵工学报, 2023, 44(6): 1744-1753.
[5]	霍健, 陈慧敏, 马云飞, 郭鹏宇, 杨旭, 孟祥盛. 基于MEMS激光雷达的车辆目标识别算法[J]. 兵工学报, 2023, 44(4): 940-948.
[6]	丁伯圣, 张睿恒, 徐立新, 陈慧敏. 基于灰度补偿和特征融合的沙尘图像修复方法[J]. 兵工学报, 2023, 44(10): 3115-3126.
[7]	王亮，陈建华，李烨. 一种基于深度学习的无人艇海上目标识别技术[J]. 兵工学报, 2022, 43(S2): 13-19.
[8]	李良福，陈卫东，高强，许开銮，刘轩，何曦，钱钧. 基于深度学习的光电系统智能目标识别[J]. 兵工学报, 2022, 43(S1): 162-168.
[9]	曹昭睿，郝永平，刘万成，白帆，孙颢洋，张慧，李宇海. 紧凑折反式仿生复眼及图像快速拼接识别算法[J]. 兵工学报, 2022, 43(8): 1845-1857.
[10]	周伯俊, 于传强，刘志浩，柯冰. 基于高压储能的特种车辆快速起竖技术与验证[J]. 兵工学报, 2022, 43(7): 1488-1497.
[11]	高月光，冯顺山，刘云辉，黄岐. 不同端盖厚度的圆柱形装药壳体破片初速分布[J]. 兵工学报, 2022, 43(7): 1527-1536.
[12]	薛灵芝, 曾向阳, 杨爽. 基于生成对抗网络的水声目标识别算法[J]. 兵工学报, 2021, 42(11): 2444-2452.
[13]	黄莎玲，朱鸿志，程祥利，谢玉斌. 自适应阈值侵彻引信层目标识别算法[J]. 兵工学报, 2020, 41(9): 1762-1771.
[14]	王健，秦春霞，杨珂，任萍. 基于导向重构与降噪稀疏自编码器的合成孔径雷达目标识别[J]. 兵工学报, 2020, 41(9): 1861-1870.
[15]	邹汝平，刘建书. 基于概率假设密度滤波与无迹Kalman滤波的多目标跟踪与识别[J]. 兵工学报, 2020, 41(8): 1502-1508.

基于熵特征的调频引信目标与干扰信号识别

Recognition of Target and Jamming Signal for FM Fuze Based on Entropy Features

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价