Submit Manuscript
Peer Review
Editor Work
| Citation: | LIU Zhangmeng, YUAN Shuo, and KANG Shiqian. Semantic coding and model reconstruction of multifunctional radar pulse train[J]. Journal of Radars, 2021, 10(4): 559–570. doi: 10.12000/JR21031
|
Semantic Coding and Model Reconstruction of Multifunction Radar Pulse Train
DOI: 10.12000/JR21031
More Information-
Abstract
Retrieving the working modes of multifunction radar from electronic reconnaissance data is a difficult problem, and it has attracted widespread attention in the field of electronic reconnaissance. It is also an important task when extracting benefits from big electromagnetic data and provides straightforward support to applications, such as radar type recognition, working state recognition, radar intention inferring, and precise electronic jamming. Based on the assumption of model simplicity, this study defines a complexity measurement rule for multifunction radar pulse trains and introduces the semantic coding theory to analyze the temporal structure of multifunction radar pulse trains. The model complexity minimization criterion guides the semantic coding procedure to extract radar pulse groups corresponding to different radar functions from pulse trains. Furthermore, based on the coded sequence of the pulse train, the switching matrix between different pulse groups is estimated, and the hierarchical working model of multifunction radars is ultimately reconstructed. Simulations are conducted to verify the feasibility and performance of the new method. Simulation results indicate that the coding theory is successfully used in the proposed method to automatically extract pulse groups and rebuild operating models based on multifunction radar pulse trains. Moreover, the method is robust to data noises, such as missing pulses. -
-
References
[1] RICHARDS M A, SCHEER J A, and HOLM W A. Principles of Modern Radar: Basic Principles[M]. Raleigh: SciTech Publishing, 2010: 33–36.[2] 张光义, 赵玉洁. 相控阵雷达技术[M]. 北京: 电子工业出版社, 2006: 1–6.ZHANG Guangyi and ZHAO Yujie. Phased Array Radar Technology[M]. Beijing: Publishing House of Electronics Industry, 2006: 1–6.[3] MELVIN W L and SCHEER J A. Principles of Modern Radar: Radar Applications[M]. Edison: SciTech Publishing, 2014: 8–14.[4] WILEY R G. ELINT: The Interception and Analysis of Radar Signals[M]. Norwood: Artech House, 2006: 1–5.[5] VISNEVSKI N, KRISHNAMURTHY V, WANG A, et al. Syntactic modeling and signal processing of multifunction radars: A stochastic context-free grammar approach[J]. Proceedings of the IEEE, 2007, 95(5): 1000–1025. doi: 10.1109/JPROC.2007.893252[6] VISNEVSKI N A. Syntactic modeling of multi-function radars[D]. [Ph. D. dissertation], McMaster University, 2005.[7] LIU Zhangmeng. Recognition of multifunction radars via hierarchically mining and exploiting pulse group patterns[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(6): 4659–4672. doi: 10.1109/TAES.2020.2999163.[8] 方佳璐. 雷达信号工作模式识别研究[D]. [硕士论文], 浙江大学, 2017.FANG Jialu. Research of radar signal patter recognition[D]. [Master dissertation], Zhejiang University, 2017.[9] LI Yunjie, ZHU Mengtao, MA Yihao, et al. Work modes recognition and boundary identification of MFR pulse sequences with a hierarchical Seq2seq LSTM[J]. IET Radar, Sonar & Navigation, 2020, 14(9): 1343–1353. doi: 10.1049/iet-rsn.2020.0060[10] 欧健. 多功能雷达行为辨识与预测技术研究[D]. [博士论文], 国防科技大学, 2017.OU Jian. Research on behavior recognition and prediction techniques against multi-function radar[D]. [Ph. D. dissertation], National University of Defense Technology, 2017.[11] OU Jian, CHEN Yongguang, ZHAO Feng, et al. Research on extension of hierarchical structure for multi-function radar signals[C]. 2017 Progress in Electromagnetics Research Symposium-Spring, St. Petersburg, Russia, 2017.[12] 林令民. 雷达语义结构分析算法设计与应用[D]. [硕士论文], 北京邮电大学, 2017.LIN Lingmin. Design and application of radar semantic structure analysis algorithm[D]. [Master dissertation], Beijing University of Posts and Telecommunications, 2017.[13] GRÜNWALD P D. The Minimum Description Length Principle[M]. Cambridge: MIT Press, 2007: 29–35.[14] SAYOOD K, 贾洪峰, 译. 数据压缩导论[M]. 4版. 北京: 人民邮电出版社, 2014: 5–8, 259–279.SAYOOD K, JIA Hongfeng, translation. Introduction on Data Compression[M]. 4th ed. Beijing: Posts & Telecom Press, 2014: 5–8, 259–279.[15] 傅祖芸. 信息论—基础理论与应用[M]. 4版. 北京: 电子工业出版社, 2015: 1–8.FU Zuyun. Information Theory—Principles and Application[M]. 4th ed. Beijing: Publishing House of Electronics Industry, 2015: 1–8.[16] ROSVALL M and BERGSTROM C T. Maps of random walks on complex networks reveal community structure[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(4): 1118–1123.[17] ROSVALL M and BERGSTROM C T. Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems[J]. PLoS One, 2011, 6(4): e18209. doi: 10.1371/journal.pone.0018209[18] TAN P N, STEINBACH M, KUMAR V, 范明, 范宏建, 译. 数据挖掘导论[M]. 2版. 北京: 人民邮电出版社, 2011: 27–38.TAN P N, STEINBACH M, KUMAR V, FAN Ming and FAN Hongjian, translation. Introduction to Data Mining[M]. 2nd ed. Beijing: Posts & Telecom Press, 2011: 27–38.[19] LIU Zhangmeng, KANG Shiqian, and CHAI Xianming. Automatic pulse repetition pattern reconstruction of conventional radars[J]. IET Radar, Sonar & Navigation, 2021, 15(5): 500–509. doi: 10.1049/rsn2.12053[20] JAEGER H. Observable operator models for discrete stochastic time series[J]. Neural Computation, 2000, 12(6): 1371–1398. doi: 10.1162/089976600300015411[21] JAEGER H, HAYKIN S, PRINCIPE J, SEJNOWSKI T, et al.. Learning Observable Operator Models Via the ES Algorithm[M]. New Directions in Statistical Signal Processing: From Systems to Brains. Cambridge: MIT Press, 2005.[22] BENGIO Y. Markovian models for sequential data[J]. Neural Computing Surveys, 1999, 2: 129–162.[23] RABINER L R. A tutorial on hidden Markov models and selected applications in speech recognition[J]. Proceedings of the IEEE, 1989, 77(2): 257–286. doi: 10.1109/5.18626 -
Proportional views
- Publishing Ethics
- Journal Insights
- Abstracting & Indexing
- Peer Review Policies
- Guide for Authors
- Conference
- ISSN 2095-283X (Print)ISSN 2097-339X (Online)
- CN 10-1030/TN
- CODEN LXEUAO
About Journal
- Sponsor: China Radio Detection and Ranging Industry Association (CRIA)
- Phone: 010-58887062
- Email：radars@aircas.ac.cn
- Publisher: Leida Xuebao Bianjibu (Editorial office of the Journal of Radars)
Contacts Us
京ICP备20021838号-8
Supported by: Beijing Renhe Information Technology Co. Ltd
Export File
Citation
Format
Content
DownLoad:
- Figure 1. Schematic diagram of the working mode of multifunctional radar
- Figure 2. Pulse group structure of multifunctional radar
- Figure 3. Hierarchical temporal structure of multifunctional radar pulse train
- Figure 4. Semantic information is contained in the hierarchical structure of multifunctional radar pulse train
- Figure 5. Schematic diagram of reconnaissance pulse train of multifunctional radar
- Figure 6. Optimization process of the coding model of multifunctional radar pulse train
- Figure 7. Estimation accuracy of the state transition matrix of multifunction radar pulse group with respect to different number of pulse groups
- Figure 8. Probability of correct pulse group reconstruction with respect to different missing ratios of pulse groups