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Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting

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Abstract

In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio (SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than -4 dB, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.

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References

  1. KAWALEC A, OWCZAREK R. Radar emitter recognition using intrapulse data [C]// 15th International Conference on Microwaves, Radar and Wireless Communications. Mikon, 2004: 435–438.

    Google Scholar 

  2. DING Jun, LIU Zhi-cheng. A study of radar emitter recognition based on characteristic parameter matching method [J]. Modern Radar, 2011, 33(9): 29–33. (in Chinese)

    MathSciNet  Google Scholar 

  3. XU Hong-quan, LIU Geng, Reviews on radar emitter recognition [J]. Ship Electronic Engineering, 2010, 30(4): 25–27. (in Chinese)

    Google Scholar 

  4. LU Man-jun, ZHAN Yi, SI Xi-cai, YANG Xiao-niu. Individual identification of 2FSK signal based on fine feature analysis of instantaneous frequency [J]. Systems Engineering and Electronics, 2009, 31(5): 1043–1046. (in Chinese)

    Google Scholar 

  5. YU Zhi-bin, CHEN Chun-xia, JIN Wei-dong. Radar emitter signal recognition based on fusion entropy features [J]. Modern Radar, 2010, 32(1): 34–38. (in Chinese)

    Google Scholar 

  6. ZHANG Ge-xiang, HU Lai-zhao, JIN Wei-dong. Radar emitter signal recognition based on entropy features [J]. Chinese Journal of Radio Science, 2005, 20(4): 440–445. (in Chinese)

    Google Scholar 

  7. JIANG Shao-fei, FU Chun, WU Zhao-qi. Intelligent data-fusion model using correlation fractal dimension for structural damage identification [J]. Smart Materials and Intelligent Systems, 2011, 143(8): 1300–1304.

    Google Scholar 

  8. HUANG Jing-ying, PAN Hong-xia, BI Shi-hua. Bi-spectrum entropy feature extraction and its application for fault diagnosis of gearbox [C]// Proceedings of 2010 IEEE International Conference on Fuzzy Systems. Taiwan, 2010: 1–6.

    Google Scholar 

  9. WANG Yu-e, ZHANG Tian-qi, BAI Juan, BAO Rui. Modulation identification algorithms for communication signals based on particle swarm optimization and support vector machines [J]. Transmitting and Receiving, 2011, 35(23): 106–110. (in Chinese)

    Google Scholar 

  10. REN Ming-qiu, CAI Jin-yan, ZHU Yuan-qing, HE Ming-hao. Radar emitter signal classification based on mutul information and fuzzy support vector machines [C]// 9th International Conference on Signal Processing. Beijing, 2008: 1641–1646.

    Google Scholar 

  11. YU De-jie, YANG Yu, CHENG Jun-sheng. Application of time-frequency entropy method based on Hilbert-Huang transform to gear fault diagnosis [J]. Measurement, 2007, 40(2): 823–830.

    Article  Google Scholar 

  12. BERCHER J F, VIGNAT C. Estimating the entropy of a signal with applications [J]. IEEE Transactions on Signal Processing, 2000, 49(6): 1687–1694.

    Article  Google Scholar 

  13. HAMED M, NUNO V. Cost-sensitive boosting [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(2): 294–309.

    Article  Google Scholar 

  14. HAN Jia-wei, KAMBER M. Data mining: Concepts and techniques [M]. 2nd ed. Beijing: China Machine Press, 2006: 289–326.

    Google Scholar 

  15. DAVID R, NOMAN P. BEAULIEU C. A comparison of SNR estimation techniques for the AWGN channel [J]. IEEE Transactions on Communications, 2000, 48(10): 1681–1691.

    Article  Google Scholar 

  16. SUI Dan, GE Lin-dong. On the blind SNR estimation for IF signals [C]// Proceedings of the First International Conference on Innovative Computing, Information and Control. Beijing, 2006: 374–378.

    Google Scholar 

  17. JOHANNA V, HARRI S, JANNE L, LEHTOMAKI, MARKKU J. A blind signal localization and SNR estimation method [C]// 2006 Military Communications Conference. Washington, DC, 2006: 1–7.

    Google Scholar 

  18. PARK C, CHOI J, NAH S, JANG W. Automatic recognition of digital signals using wavelet features and SVM [C]// 10th International Conference on Advanced Communication Technology. Gangwon-Do, 2008: 387–390.

    Google Scholar 

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Authors and Affiliations

  1. Department of Information and Communication Engineering, University of Harbin Engineering, Harbin, 150001, China

    Yi-bing Li  (李一兵), Juan Ge  (葛娟), Yun Lin  (林云) & Fang Ye  (叶方)

Authors
  1. Yi-bing Li  (李一兵)
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  2. Juan Ge  (葛娟)
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  3. Yun Lin  (林云)
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  4. Fang Ye  (叶方)
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Corresponding author

Correspondence to Juan Ge  (葛娟).

Additional information

Foundation item: Project(61301095) supported by the National Natural Science Foundation of China; Project(QC2012C070) supported by Heilongjiang Provincial Natural Science Foundation for the Youth, China; Projects(HEUCF130807, HEUCFZ1129) supported by the Fundamental Research Funds for the Central Universities of China

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Li, Yb., Ge, J., Lin, Y. et al. Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting. J. Cent. South Univ. 21, 4254–4260 (2014). https://doi.org/10.1007/s11771-014-2422-5

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  • DOI: https://doi.org/10.1007/s11771-014-2422-5

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