基于小波包字典稀疏表示的调频引信目标识别方法

doi:10.12305/j.issn.1001-506X.2026.01.03

系统工程与电子技术 ›› 2026, Vol. 48 ›› Issue (1): 22-33.doi: 10.12305/j.issn.1001-506X.2026.01.03

基于小波包字典稀疏表示的调频引信目标识别方法

刘冰¹^,²^,*(), 刘佳琪¹, 时明心¹, 郝新红³

1. 中国民航大学交通科学与工程学院，天津 300300
2. 天津市城市空中交通系统技术与装备重点实验室，天津 300300
3. 北京理工大学机电动态控制重点实验室，北京 100081

收稿日期:2024-06-17 出版日期:2026-01-25 发布日期:2026-02-11
通讯作者: 刘冰 E-mail:liub@cauc.edu.cn
作者简介:刘佳琪（2002—）女，硕士研究生，主要研究方向为模式识别
时明心（2001—）男，硕士研究生，主要研究方向为机器学习、目标探测
郝新红（1976—）女，教授，博士，主要研究方向为信息感知、无线电探测
基金资助:
国家自然科学基金青年项目（62401568）；天津市城市空中交通系统技术与装备重点实验室开放课题基金（TJKL-UAM-202403）；云南省无人自主系统重点实验室开放课题（202501YB02）；警用智能机器人达州市重点实验室开放课题（JYZJ202512）资助课题

Frequency modulation fuze target recognition method based on sparse representation of wavelet packet dictionary

Bing LIU¹^,²^,*(), Jiaqi LIU¹, Mingxin SHI¹, Xinhong HAO³

1. College of Transporation Science and Engineering，Civil Aviation University of China，Tianjin 300300，China
2. Laboratory of Technology and Equipment of Tianjin Urban Air Transportation System，Tianjin 300300，China
3. Science and Technology of Electromechanical Dynamic Control Laboratory，Beijing Institute of Technology，Beijing 100081，China

Received:2024-06-17 Online:2026-01-25 Published:2026-02-11
Contact: Bing LIU E-mail:liub@cauc.edu.cn

摘要/Abstract

摘要：

针对复杂的战场电磁环境对无线电引信构成干扰后引发早炸、削弱击打效能的情况，对调频引信干扰威胁最大的扫频式干扰，提出一种基于小波包字典稀疏表示的调频目标识别方法。选取三层小波包熵特征作为原子构建稀疏字典，引信检波端输出信号在小波包稀疏字典进行稀疏分解，将稀疏表示系数进行稀疏重构，通过重构误差最小原则，确定引信目标信号的类别。调频无线电引信样机的实测数据进行实验验证，验证结果为目标识别准确率达到98.72%，干扰识别准确率达到97.94%。这表明了所提方法能够有效的对调频引信目标信号和几种典型扫频式干扰信号进行有效的识别。

关键词: 电子战, 无线电引信, 小波包字典, 分类识别, 抗干扰

Abstract:

Aiming at the situation that the complex battlefield electromagnetic environment jamming with the radio fuze, which causes early explosion and weakens the striking efficiency, a frequency modulation target recognition method based on sparse representation of wavelet packet dictionary is proposed for the sweep frequency jamming, which is the most threatening jamming to the frequency modulation fuze. The sparse dictionary is constructed by using the three-layer wavelet packet entropy feature as the atom. The output signal of the fuze detection end is sparsely decomposed in the wavelet packet sparse dictionary, and the sparse representation coefficient is sparsely reconstructed. The classification of the fuze target signal is determined by the principle of minimum reconstruction error. The experimental verification of the measured data of the frequency modulation radio fuze prototype shows that the accuracy of target recognition reaches 98.72%, and the accuracy of interference recognition reaches 97.94%. This shows that the proposed method can effectively recognize the target signal of frequency modulation fuze and several typical sweep frequency jamming signals.

Key words: electronic warfare, radio fuze, wavelet packet dictionary, classification and recognition, anti-jamming

中图分类号:

TJ 43+4.1

刘冰, 刘佳琪, 时明心, 郝新红. 基于小波包字典稀疏表示的调频引信目标识别方法[J]. 系统工程与电子技术, 2026, 48(1): 22-33.

Bing LIU, Jiaqi LIU, Mingxin SHI, Xinhong HAO. Frequency modulation fuze target recognition method based on sparse representation of wavelet packet dictionary[J]. Systems Engineering and Electronics, 2026, 48(1): 22-33.

图/表 12

图1

图2

表1

图3

图4

图5

图6

图7

图8

图9

图10

表2

参考文献 31

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.
	ZHAO H C. Fundamentals and methodology of radio fuze [M]. Beijing: National Defense Industry Press, 2012.
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.
	CUI Z Z, SONG S H, XU L X. Principle of proximity fuze[M]. 3 rd ed. Beijing: Beijing Institute of Technology Press, 2009.
3	李剑锋, 代健, 郝新红, 等. 无线电引信认知抗干扰模型及关键技术综述[J]. 探测与控制学报, 2022, 44 (5): 1- 9.
	LI J F, DAI J, HAO X H, et al. Review of cognitive anti-jamming model and key technology of radio fuze[J]. Journal of Detection & Control, 2022, 44 (5): 1- 9.
4	郝新红, 杜涵宇, 陈齐乐. 调频引信粗糙面目标与干扰信号识别[J]. 北京航空航天大学学报, 2019, 45 (10): 1946- 1955.
	HAO X H, DU H Y, CHEN Q L. Rough surface target and jamming signal recognition of FM fuze[J]. Journal of Beijing University ofAeronautics and Astronautics, 2019, 45 (10): 1946- 1955.
5	DAI J, HAO X H, LI Z, et al. Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-up-dated naive bayesian classifier with minimal risk[J]. Defence Technology, 2021, 19 (3): 128- 140.
6	韩磊, 周帅. 基于FLAKNN的雷达一维距离像目标识别[J]. 北京理工大学学报, 2021, 41 (6): 611- 618. doi: 10.15918/j.tbit1001-0645.2020.181
	HAN L, ZHOU S. Radar range profile target recognition based on FLAKNN[J]. Transcations of Beijing Institute of Technology, 2021, 41 (6): 611- 618. doi: 10.15918/j.tbit1001-0645.2020.181
7	严保康, 周凤星, 徐波. 基于GST的变速机械故障信号稀疏特征提取方法[J]. 北京理工大学学报, 2019, 39 (6): 603- 608. doi: 10.15918/j.tbit1001-0645.2019.06.009
	YAN B K, ZHOU F X, XU B. Sparse feature extraction for variable speedmachinery based on sparse decomposition combined GST[J]. Transcations of Beijing Institute of Technology, 2019, 39 (6): 603- 608. doi: 10.15918/j.tbit1001-0645.2019.06.009
8	马俊涛, 高梅国, 郭宝峰, 等. 基于二维稀疏特性的空间目标高分辨ISAR成像方法[J]. 北京理工大学学报, 2018, 38(5): 511−518.
	MA J T, GAO M G, GUO B F, et al. High resolution ISAR imaging of space target based on two-dimensional sparse characteristics [J]. Transcations of Beijing Institute of Technology, 2018, 38(5): 603−608.
9	余发军, 刘义才. 基于改进量子进化算法的稀疏特征提取方法[J]. 北京理工大学学报, 2020, 40 (5): 512- 518.
	YU F J, LIU Y C. A sparse feature extraction method based on improved quantum evolutionary algorithm[J]. Transcations of Beijing Institute of Technology, 2020, 40 (5): 512- 518.
10	BAI Y C, TANG M. Object tracking via robust multitask sparse representation[J]. IEEE Signal Processing Letters, 2014, 21 (8): 909- 913. doi: 10.1109/LSP.2014.2320291
11	ZHANG Z, ZHAO M B, TOMMY W S. Binary and multi-class group sparsecanonical correlation analysis for feature extraction and classification[J]. IEEE Trans. on Knowledgeand Data Engineering, 2013, 25 (10): 2129- 2205.
12	SINGH G, CHILUVERU S R, RAMAN B, et al. Novel architecture for liftingdiscrete wavelet packet transform with arbitrary tree structure[J]. IEEE Trans. on Very Large Scale Integration （VLSI） Systems, 2021, 29 (7): 1490- 1494.
13	SHI J, LIU X P, XIANG W, et al. Novel fractional wavelet packet transform: theory, implementation, and applications[J]. IEEE Trans. on Signal Processing, 2020, 68 (7): 4041- 4054.
14	PANCHOLI S, JOSHI A M. Improved classification scheme using fused wavelet packet transform based features for intelligent myoelectric prostheses[J]. IEEE Trans. on Industrial Electronics, 2020, 67 (10): 8517- 8525. doi: 10.1109/TIE.2019.2946536
15	PUN C M, LEE M C. Extraction of shift invariant wavelet features for classification of images with different sizes[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2004, 26 (9): 1228- 1233. doi: 10.1109/TPAMI.2004.67
16	HU Q, QIN A S, ZHANG Q H, et al. Fault diagnosis based on weighted extreme learning machine with wavelet packet decomposition and KPCA[J]. IEEE Sensors Journal, 2018, 18 (20): 8472- 8483. doi: 10.1109/JSEN.2018.2866708
17	HE H, CHENG S J, ZHANG Y B. Home network power-line communication signal processing based on wavelet packet analysis[J]. IEEE Trans. on Power Delivery, 2005, 20 (3): 1879- 1885. doi: 10.1109/TPWRD.2004.843489
18	SALVADOR M Z, RESMINI R G, GOMEZ R B. Detection of sulfur dioxide in AIRS data with the wavelet packet subspace[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6 (1): 137- 141. doi: 10.1109/LGRS.2008.2009645
19	ROSSANT F , MIKOVICOVA B , ADAM M ,et al.A robust iris identification system based on wavelet packet decomposition and local comparisons of the extracted signatures[J].EURASIP Journal on Advances in Signal Processing, 2010.DOI:10.1155/2010/415307.
20	WEICKERT T, BENJAMINSEN C. Analytic wavelet packets—combining the dual-tree approach with wavelet packets for signal analysis and filtering[J]. IEEE Trans. on Signal Processing, 2009, 57 (2): 493- 502. doi: 10.1109/TSP.2008.2007922
21	ZHA X, FU R, DAI Z. Noise reduction in interferograms using the wavelet packet transform and wiener filtering[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5 (3): 404- 408. doi: 10.1109/LGRS.2008.916066
22	BRECHET L, LUCAS M F. Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection[J]. IEEE Trans. on Biomedical Engineering, 2007, 54 (12): 2186- 2192. doi: 10.1109/TBME.2007.896596
23	GYANENDRA S R, CHILUVERU B, RAMAN. Memory efficient architecture for lifting-based discrete wavelet packet transform[J] IEEE Trans. on Circuits and Systems II: Express Briefs, 2021, 68(4): 1373−1377.
24	ANDREOPOULOS Y. Generalized phase shifting for M-band discrete wavelet packet transforms[J]. IEEE Trans. on Signal Processing, 2007, 55 (2): 742- 747. doi: 10.1109/TSP.2006.885743
25	KARMAKAR A, KUMAR. Design of optimal wavelet packet trees based on auditory perception criterion[J]. IEEE Signal Processing Letters, 2007, 14 (4): 240- 243. doi: 10.1109/LSP.2006.884129
26	MOHAMED K, MASHAEL A. Shannon differential entropy properties of consecutive k-out-of-n: G systems[J]. Operations Research Letters, 2024, 57, 107190. doi: 10.1016/j.orl.2024.107190
27	MOHSEN Z, COLE D, DONALD J. Largest Lyapunov exponent and Shannon entropy: Two indices to analyze mixing in fluidized beds[J]. Chemical Engineering Research and Design, 2024, 210 (10): 59- 70.
28	SERGIO J, MARTÍNEZ G, PABLO P. Analysis of Shannon’s entropy to contrastbetween the Embodied and Neurocentrist hypothesis of conscious experience[J] BioSystems, 2024, 26(12): 105323.
29	ZHENG Y, ZHENG P. Image matching based on Fast PCA-SIFT descriptorswith automatic determination of dimensionality for PCA[C]// Proc. of the IEEE 2nd International Conference on Information Communication and Software Engineering, 2022: 115−120.
30	REHMAN A, KHAN A, ALI M A. Performance analysis of PCA, sparse PCA, kernel PCA and incremental PCA algorithms for heart failure prediction[C]// Proc. of the International Conference on Electrical, Communication, and Computer Engineering, 2020.
31	VO D M, SUNGYOUNG L. Two-dimensional weighted PCA algorithm for face recognition[C]// Proc. of the International Symposium on Computational Intelligence in Robotics and Automation, 2005: 219−223.

小波包节点	Kruskal-Wallis检验$ p $值
（0,0）	$ 1.406\;8{\text{ }} \times {\text{ }}{10^{ - 47}} $
（1,0）	$ 9.8\;566{\text{ }} \times {\text{ }}{10^{ - 48}} $
（1,1）	$ 3.401\;5{\text{ }} \times {\text{ }}{10^{ - 70}} $
（2,0）	$ 8.354\;7{\text{ }} \times {\text{ }}{10^{ - 48}} $
（2,1）	$ 1.349\;7{\text{ }} \times {\text{ }}{10^{ - 61}} $
（2,2）	$ 3.268\;8{\text{ }} \times {\text{ }}{10^{ - 70}} $
（2,3）	$ 5.841\;0{\text{ }} \times {\text{ }}{10^{ - 7}} $
（3,0）	$ 6.741\;2{\text{ }} \times {\text{ }}{10^{ - 48}} $
（3,1）	$ 0.388\;3 $
（3,2）	$ 1.527\;8{\text{ }} \times {\text{ }}{10^{ - 35}} $
（3,3）	$ 1.056\;8{\text{ }} \times {\text{ }}{10^{ - 47}} $
（3,4）	$ 9.076\;3{\text{ }} \times {\text{ }}{10^{ - 70}} $
（3,5）	$ 2.251\;8{\text{ }} \times {\text{ }}{10^{ - 54}} $
（3,6）	$ 1.643\;6{\text{ }} \times {\text{ }}{10^{ - 22}} $
（3,7）	$ 1.418\;7{\text{ }} \times {\text{ }}{10^{ - 9}} $

指标	结果/%
目标识别准确率$ {\mathrm{Acc}}\_T $	98.72
扰识别准确率$ {\mathrm{Acc}}\_J $	97.94
被干扰概率$ {\mathrm{Dist}}\_r $	2.06

[1]	李开明, 张袁鹏, 罗迎, 代肖楠. 弹道导弹雷达目标识别研究进展[J]. 系统工程与电子技术, 2025, 47(9): 2870-2889.
[2]	龙志涛, 程志君, 封琛. 基于耦合约束满足的雷达抗干扰试验样本空间特征分析[J]. 系统工程与电子技术, 2025, 47(7): 2136-2145.
[3]	赵正义, 南普龙. 基于短时傅里叶变换的高效空频自适应抗干扰方法[J]. 系统工程与电子技术, 2025, 47(6): 1778-1785.
[4]	刘书含, 李彤, 李富强, 杨春刚. 意图态势双驱动的数据链抗干扰通信机制[J]. 系统工程与电子技术, 2025, 47(6): 2055-2064.
[5]	贾金伟, 高敏, 韩壮志, 刘利民, 尹园威. 无线电近炸引信抗信息型干扰技术研究综述[J]. 系统工程与电子技术, 2025, 47(4): 1074-1107.
[6]	王子怡, 傅雄军, 董健, 冯程. 基于分层多智能体强化学习的雷达协同抗干扰策略优化[J]. 系统工程与电子技术, 2025, 47(4): 1108-1114.
[7]	马鹏, 蒋睿, 王斌, 徐盟飞, 侯长波. 基于隐式对手建模的策略重构抗智能干扰方法[J]. 系统工程与电子技术, 2025, 47(4): 1355-1363.
[8]	陈世龙, 刘霖, 王晓蓓, 曾翰森, 刘亚波, 刘翔. 脉间捷变频SAR快速补偿频域成像处理算法[J]. 系统工程与电子技术, 2025, 47(3): 797-806.
[9]	高飞, 席睿达, 邢相薇, 邢妍, 张强, 党红杏. SAR有源干扰分类识别图像数据集仿真构建[J]. 系统工程与电子技术, 2025, 47(10): 3257-3269.
[10]	于雷, 刘一品, 位寅生. 基于信干噪比最大化的盲提取抗主瓣干扰方法[J]. 系统工程与电子技术, 2024, 46(9): 2968-2979.
[11]	张施雨, 吴煜, 熊怡因. 地形遮蔽下的电磁波绕射效应对机载电子战影响[J]. 系统工程与电子技术, 2024, 46(4): 1185-1192.
[12]	张嘉毫, 李伟, 王万田, 王衡峰, 李亚星, 孟进. 阵列自由度欠定条件下雷达干扰对消方法及性能分析[J]. 系统工程与电子技术, 2024, 46(11): 3639-3647.
[13]	韩静雯, 杨勇, 连静, 吴国庆, 王雪松. 基于极化与距离像特征融合的雷达导引头角反射器鉴别方法[J]. 系统工程与电子技术, 2024, 46(11): 3658-3670.
[14]	姜丽敏, 陈曙暄, 路坤峰. 基于干扰观测器的飞行器主动抗干扰控制方法[J]. 系统工程与电子技术, 2024, 46(11): 3883-3892.
[15]	高宏璋, 葛松虎, 郭宇, 刘让, 孟进. 交替极化阵列对消盲区分析与验证[J]. 系统工程与电子技术, 2024, 46(1): 51-61.

基于小波包字典稀疏表示的调频引信目标识别方法

Frequency modulation fuze target recognition method based on sparse representation of wavelet packet dictionary

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 31

相关文章 15

编辑推荐

Metrics

本文评价