Anti-jamming Method for Pulse Doppler Fuze Based on Joint Feature Extraction of Target Signal

doi:10.3969/j.issn.1000-1093.2019.02.001

Abstract

Abstract: An anti-jamming method based on the joint feature extraction of target signal for pulse Doppler (PD) fuze is proposed for the inadequate capacity of PD fuze against information type jamming. The output signal of fuze baseband filter is analyzed in consideration with the target function, and an expected target space is constructed using both two-class and one-class support vector machines (SVMs). The proposed method is verified through prototype test. The experimental results indicate that the prototype of PD fuze achieves more than 90 percent success rate in anti-jamming, which can significantly improve the anti-jamming ability of pulse Doppler fuze. Key

Key words: pulseDopplerfuze, targetfunction, featureextraction, supportvectormachine

CLC Number:

TJ43⁺4.1

DAI Jian, LI Ze, HAO Xinhong, LI Ping. Anti-jamming Method for Pulse Doppler Fuze Based on Joint Feature Extraction of Target Signal[J]. Acta Armamentarii, 2019, 40(2): 225-233.

References

［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，2009:104-116.
CUI Z Z，SONG S H，XU L X. Principle of proximity fuze[M]. 3rd ed. Beijing: Beijing Institute of Technology Press, 2009:104-116. (in Chinese)
[2］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, 2012:147-162.
ZHAO H C. Principle and method of radio fuze design[M]. Beijing：National Defense Industry Press, 2012:147-162. (in Chinese)
[3］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 Z，LI P，HAO X H，et al. Anti-active noise jamming perfor- mance of pulse Doppler fuze[J]. Acta Armamentarii, 2015,36(6): 1001-1008. (in Chinese)
[4］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 S K，YAN X P，LI P，et al. Anti-periodic modulation jamming performance of pulse Doppler fuze[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017,69(10):1-9. (in Chinese)
[5］李月琴,闫晓鹏,杭和平,等.基于模糊综合评判的无线电引信抗干扰性能评估[J].兵工学报,2016,37(5):791-797.
LI Y Q，YAN X P, HANG H P, et al. Evaluation of anti-jamming performance of radio fuze based on fuzzy comprehensive judgment[J]. Acta Armamentarii, 2016,37(5):791-797. (in Chinese)
[6］涂友超,赵惠昌,周新刚.伪码体制引信抗瞄准式噪声调幅干扰性能研究[J].南京理工大学学报，2008,32(3): 350-355.
TU Y C，ZHAO H C, ZHOU X G. Performance of anti-noise AM spot jamming of pseudo-random code fuzes[J]. Journal of Nanjing University of Science and Technology，2008,32(3):350-355. (in Chinese)
[7］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 Y C，ZHAO H C，ZHOU X G. Analysis on starting 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)
[8］隋鉴,李国林.针对脉冲多普勒引信的二维多假目标干扰[J].电光与控制,2016,23(3):7-10.
SUI J, LI G L. Two-dimensional multiple false targets jamming against pulse Doppler fuze[J]. Electronics Optics & Control, 2016,23(3):7-10. (in Chinese)
[9］周新刚,赵惠昌,徐元银.伪码调相PD引信抗干扰性能测度理论和方法[J].南京理工大学学报,2010,34 (2):176-181.
ZHOU X G, ZHAO H C, XU Y Y. Theory and method for evaluation of anti-jamming capability of phase modulated by pseudorandom code combined with pulse Doppler fuze[J]. Journal of Nanjing University of Science and Technology, 2010,34 (2):176-181. (in Chinese)
[10］谢雪康,杨晓蓉.复杂电磁环境下伪随机码引信信号抗干扰性分析[J].电子信息对抗技术,2010,25(3):48-53.
XIE X K, YANG X R. Analysis of anti-jamming to pseudo-random code fuse signal[J]. Electronic Information Warfare Technology, 2010,25(3):48-53. (in Chinese)
[11］李泽,闫晓鹏,栗苹,等.基于相似度的无线电引信抗干扰能力量化表征方法[J].兵工学报,2017,38(7): 1282-1288.
LI Z, YAN X P, LI P, et al. Similarity-based quantification method for anti-jamming performance of radio fuze[J]. Acta Armamentarii, 2017, 38(7):1282-1288. (in Chinese)
[12］ YUAN Y J, HUANG Z T, WU H, et al. Specific emitter identification based on Hilbert-Huang transform-based time-frequency-energy distribution features[J]. IET Communications, 2014, 8(13): 2404-2412.
[13］ BERTONCINI C, RUDD K, NOUSAIN B, et al. Wavelet fingerprinting of radio-frequency identification (RFID) tags[J]. IEEE Transactions on Industrial Electronics, 2012, 59(12):4843-4850.
[14］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 L，PAN H，FENG Y，et al. Pattern recognition[M]. Wuhan: Wuhan University Press, 2006：123-129. (in Chinese)

第40卷
第2期2019 年2月兵工学报ACTA
ARMAMENTARIIVol.40No.2Feb.2019

[1]	DUAN Hao， CHEN Hui， ZHAI Zhaoyang， HAN Yu， MA Fanhua， CUI Yahui. Performance Prediction of Hydrogen-enriched Compressed Natural Gas Engine Based on Support Vector Machine [J]. Acta Armamentarii, 2022, 43(5): 1002-1011.
[2]	YANG Jianxin， LAN Xiaoping， FENG Yadong， YANG Yiming， GUO Zhiming. An Ammunition Quality Evaluation Method Based on Least Squares Support Vector Machine [J]. Acta Armamentarii, 2022, 43(5): 1012-1022.
[3]	LI Liangyu， SU Tiexiong， MA Fukang， WU Xiaojun， XU Chunlong. Fault Diagnosis Method of High-pressure Common Rail System Based on EEMD-SVM [J]. Acta Armamentarii, 2022, 43(5): 992-1001.
[4]	LI Heng, YE Zukun, ZHA Wenbin, WANG Yulin. Tool Wear Online Monitoring Based on Multi-sensor Information Decision-making Level Fusion [J]. Acta Armamentarii, 2021, 42(9): 2024-2031.
[5]	MA Runbo， DONG Lihong， WANG Haidou. Prediction of Contact Fatigue Life of Thermal Sprayed Coating Based on Support Vector Machine [J]. Acta Armamentarii, 2021, 42(12): 2743-2752.
[6]	LEI Zhenshuo, LIU Songtao, WEN Zhenming, GE Yang. A Real-time Discrimination Method for Operating State of Terminal Guidance Radar in Complex Electromagnetic Environment [J]. Acta Armamentarii, 2021, 42(1): 83-90.
[7]	WANG Cheng， WU Yan， YANG Tingfei. Improving the Detection Accuracy of Wake Targets by Improved One-class Support Vector Machine [J]. Acta Armamentarii, 2020, 41(9): 1887-1893.
[8]	YU Honghai， YAN Xiaopeng， JIA Ruili， LI Ze， WANG Jiantao. Research on Anti-jamming Performance of M-sequence Pseudo-random Code Phase Modulation Pulse Doppler Fuze [J]. Acta Armamentarii, 2020, 41(3): 417-425.
[9]	L Guojun， CAO Jianjun， ZHENG Qibin， CHANG Chen， WENG Nianfeng， PENG Cong. Detection of Similar Duplicate Records Based on OCSVM and Multi-objective Ant Colony Optimization [J]. Acta Armamentarii, 2020, 41(2): 324-331.
[10]	YU Yongwei， PENG Xi， DU Liuqing， CHEN Tianhao. Real-time Detection of Parts by Assembly Robot Based on Deep Learning Framework [J]. Acta Armamentarii, 2020, 41(10): 2122-2130.
[11]	DENG Qing, XUE Qing, LUO Jia. Analysis of Tank Driving Simulation Training Results Based on Support Vector Machine [J]. Acta Armamentarii, 2019, 40(9): 1953-1960.
[12]	TONG Rui, KANG Jianshe, SUN Jian, YANG Wen, LI Baochen. Gear Performance Degradation Feature Extraction Based on Local Characteristic-scale Decomposition and Modified CompositeSpectrum Analysis [J]. Acta Armamentarii, 2019, 40(5): 1093-1102.
[13]	YANG Da-wei， ZHAO Yong-dong， FENG Fu-zhou， JIANG Peng-cheng， DING Chuang. Planetary Gearbox Fault Feature Extraction Based on Parameter Optimized Variational Mode Decomposition and Partial Mean ofMulti-scale Entropy [J]. Acta Armamentarii, 2018, 39(9): 1683-1691.
[14]	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.
[15]	YU Zhen-liang, SUN Zhi-li, CAO Ru-nan, WANG Peng. Research on Remaining Useful Life Predictive Model of Machine Parts Based on SVM and Kalman Filter [J]. Acta Armamentarii, 2018, 39(5): 991-997.