基于模糊c-均值增量更新的脉冲多普勒引信干扰与目标信号识别

doi:10.3969/j.issn.1000-1093.2018.09.006

兵工学报 ›› 2018, Vol. 39 ›› Issue (9): 1711-1718.doi: 10.3969/j.issn.1000-1093.2018.09.006

基于模糊c-均值增量更新的脉冲多普勒引信干扰与目标信号识别

代健¹，晏祺²，闫晓鹏¹，栗苹¹，李泽¹

(1.北京理工大学机电动态控制重点实验室，北京 100081; 2.中国电子科技集团有限公司光电研究院，天津 300308）

收稿日期:2018-03-05 修回日期:2018-03-05 上线日期:2018-10-25
作者简介:代健(1994—)，男，博士研究生。E-mail: daijian19940509@126.com
基金资助:
国家自然科学基金项目（61673066）；国防“973”计划项目(613196)

Recognition of Jamming and Target Signal for Pulse Doppler Fuze Based on FCM Algorithm with Incremental Update

DAI Jian¹, YAN Qi², YAN Xiao-peng¹, LI Ping¹, LI Ze¹

(1.Science and Technology on Electromechanical Dynamic Control Laboratory, Beijing Institute of Technology, Beijing 100081, China;2.Academy of Opto-Electronics, China Electronics Technology Group Corporation, Tianjin 300308, China)

Received:2018-03-05 Revised:2018-03-05 Online:2018-10-25

摘要/Abstract

摘要： 针对脉冲多普勒（PD）引信在复杂战场电磁环境中对目标回波信号识别效率不足问题，提出了一种具有自适应增量更新功能的模糊c-均值(FCM)聚类算法，以有效地提高引信对干扰和目标信号的识别率。在对引信距离门选通输出信号分析的基础上，利用信号时域与频域熵特征，借助FCM算法对干扰与目标信号进行分类识别，并针对信噪比不断恶化的情况，通过改进的增量更新算法实现引信FCM分类模型自适应更新调整，从而保持引信对干扰和目标信号的高识别率。仿真实验结果表明：改进的增量更新算法在降低更新耗时的同时，可使引信在-15 dB信噪比条件下对干扰与目标信号识别正确率达到96.43%，显著地提高了PD引信抗有源噪声干扰能力。

关键词: 脉冲多普勒引信, 有源噪声干扰, 模糊c-均值聚类, 增量更新

Abstract: A fuzzy c-means (FCM) method with self-adaptive update function is proposed to improve the target recognition accuracy of pulse Doppler (PD) fuze in the complex electromagnetic environment. Based on the analysis of the output signal from fuze range-gate, the time domain and frequency domain entropies of signal are used for the recognition of target signal and jamming by means of FCM method. A modified incremental update algorithm is used to update and adjust the FCM classification model when the signal-to-jamming ratio decreases. The simulated results indicate that the modified incremental update algorithm can not only reduce the time-consuming, but also keep the recognition accuracy of target signal and jamming at 96.43% at -15 dB SNR, which significantly improves the anti-active noise jamming ability of pulse Doppler fuze. Key

Key words: pulseDopplerfuze, activenoisejamming, fuzzyc-meansclustering, incrementalupdate

中图分类号:

TJ43⁺4.1

代健，晏祺，闫晓鹏，栗苹，李泽. 基于模糊c-均值增量更新的脉冲多普勒引信干扰与目标信号识别[J]. 兵工学报, 2018, 39(9): 1711-1718.

DAI Jian, YAN Qi, YAN Xiao-peng, LI Ping, LI Ze. Recognition of Jamming and Target Signal for Pulse Doppler Fuze Based on FCM Algorithm with Incremental Update[J]. Acta Armamentarii, 2018, 39(9): 1711-1718.

参考文献

［1］赵惠昌. 无线电引信设计原理与方法[M]. 北京: 国防工业出版社, 2012:153-161.
ZHAO Hui-chang. Fundamentals and methodology of radio fuze [M]. Beijing: National Defense Industry Press, 2012:153-161. (in Chinese)
[2］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, 2009:15-43.
CUI Zhan-zhong, SONG Shi-he, XU Li-xin. Principle of proximity fuze[M]. 3rd ed. Beijing: Beijing Institute of Technology Press, 2009:15-43. (in Chinese)
[3］冯春环, 杨硕, 丁学飞. 脉冲多普勒引信抗干扰性能数字仿真[J]. 航空兵器,2012(4):27-29.
FENG Chun-huan, YANG Shuo, DING Xue-fei. Numerical simulation of anti-jamming performance for pulse-Doppler fuze[J]. Aero Weaponry, 2012(4):27-29.(in Chinese)
[4］李泽, 栗苹, 郝新红,等. 脉冲多普勒引信抗有源噪声干扰性能研究[J]. 兵工学报,2015,36(6):1001-1008.
LI Ze, LI Ping, HAO Xin-hong, et al. Anti-active noise jamming performance of pulse Doppler fuze[J]. Acta Armamentarii, 2015,36(6):1001-1008. (in Chinese)
[5］刘少坤, 闫晓鹏, 栗苹, 等. 脉冲多普勒引信抗周期调制干扰性能研究[J]. 北京航空航天大学学报,2017,69(10):1-9.
LIU Shao-kun, YAN Xiao-peng, LI Ping, et al. Anti-periodicmodulation jamming performance of pulse Doppler fuze[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 69(10):1-9.(in Chinese)
[6］涂友超, 赵惠昌, 周新刚. 噪声调频干扰下伪码调相引信启动概率分析[J]. 南京理工大学学报,2011,35(2):252-256.
TU You-chao, ZHAO Hui-chang, ZHOU Xin-gang. Analysis onstarting probability of pseudo-random binary-phase-coded fuze under noise FM jamming [J]. Journal of Nanjing University of Science and Technology, 2011,35(2):252-256.(in Chinese)
[7］索文斌. 伪随机码脉冲多普勒引信干扰研究[D].西安:西安电子科技大学,2014:19-30.
SUO Wen-bin.Research on jamming against pseudo-random code pulse-Doppler fuze[D]. Xi'an: Xidian University, 2014:19-30.(in Chinese)
[8］王李军, 赵惠昌, 杨小牛. 低信噪比环境下的高精度伪码引信设计和分析[J]. 兵工学报,2009,30(1):9-13.
WANG Li-jun, ZHAO Hui-chang, YANG Xiao-niu. Design andanalysis of PN code fuze with high precision in low SNR condition[J]. Acta Armamentarii, 2009, 30(1):9-13.(in Chinese)
[9］ Ghosh A, Mishra N S, Ghosh S. Fuzzy clustering algorithms for unsupervised change detection in remote sensing images[J]. Information Sciences, 2011, 181(4):699-715.
[10］ Dikbas F, Firat M, Koc A C, et al. Classification of precipita- tion series using fuzzy cluster method[J]. International Journal of Climatology, 2012, 32(10):1596-1603.
[11］ Wang Y T, Chen L H, Mei J P. Incremental fuzzy clustering with multiple medoids for large data[J]. IEEE Transactions on Fuzzy Systems, 2014, 22(6):1557-1568.
[12］钟珞, 潘昊, 封筠, 等. 模式识别[M]. 武汉：武汉大学出版社, 2006:123-129.
ZHONG Luo, PAN Hao, FENG Yun, et al. Pattern recognition[M]. Wuhan: Wuhan University Press, 2006:123-129.(in Chinese)
[13］李一兵, 常国彬, 叶方. 基于指数熵的认知无线电频谱感知算法[J]. 吉林大学学报:工学版,2014,44(5):1506-1511.
LI Yi-bing.CHANG Guo-bin, YE Fang. Exponential entropy-based spectrum sensing algorithm in cognitive radio[J]. Journal of Jilin University:Engineering and Technology Edition,2014,44(5): 1506-1511.(in Chinese)
[14］ Bagirov A M, Ugon J,Webb D. Fast modified global K-means algorithm for incremental cluster construction[J]. Pattern Recognition, 2011, 44(4):866-876.

第39卷
第9期2018 年9月兵工学报ACTA
ARMAMENTARIIVol.39No.9Sep.2018

[1]	王新伟, 闫晓鹏, 郝新红, 陈齐乐, 黄鼎琨. 基于合成虚拟阵列的脉冲多普勒引信高分辨DOA估计方法[J]. 兵工学报, 2024, 45(1): 97-104.
[2]	李剑锋，闫晓鹏，郝新红，于洪海. 基于生成对抗网络的伪码调相脉冲多普勒引信引导式干扰方法[J]. 兵工学报, 2022, 43(10): 2534-2544.
[3]	于洪海，闫晓鹏，贾瑞丽，李泽，王建涛. M序列伪码调相脉冲多普勒引信抗干扰性能研究[J]. 兵工学报, 2020, 41(3): 417-425.
[4]	代健，李泽，郝新红，栗苹. 基于目标联合特征提取的脉冲多普勒引信抗干扰方法[J]. 兵工学报, 2019, 40(2): 225-233.
[5]	刘少坤，闫晓鹏，栗苹，于洪海. 基于码元重构的伪码调相脉冲多普勒引信干扰信号设计[J]. 兵工学报, 2018, 39(6): 1088-1094.
[6]	李泽，栗苹，郝新红，闫晓鹏，王建涛. 脉冲多普勒引信抗有源噪声干扰性能研究[J]. 兵工学报, 2015, 36(6): 1001-1008.

基于模糊c-均值增量更新的脉冲多普勒引信干扰与目标信号识别

Recognition of Jamming and Target Signal for Pulse Doppler Fuze Based on FCM Algorithm with Incremental Update

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 6

编辑推荐

Metrics

本文评价