Abstract
Aiming at the problems of the radar emitter signal (RES) recognition based on intra-pulse feature, a novel entropy feature extraction approach is proposed. In this method the sample entropy (SampEn) and fuzzy entropy (FzzyEn) are presented to extract features from RES. The SampEn can measure the complexity of RES from a short signal data, and the FzzyEn is used as a measure of the uncertainty. Feature vectors abstracted from 6 typical RES are used as the input of support vector machine (SVM) classifier to perform the signal recognition. Experimental result indicates that in a large range of SNR the introduced method achieves a good accuracy recognition rate. Simulation verifies the method to be feasible.
This paper was supported by the National Natural Science Foundation (No.F030408), by the National Defence Technology Keystone Laboratory Foundation (No.9140C610301080C6106; No.9140C6001070801), by the Aviation Science Foundation (No.20095596014; No. 20101996009) and supported by the Shaanxi Natural Science Foundation (No.2009JM8001-4).
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References
Wiley, R.G.: ELINT: The interception and analysis of radar signals, 2nd edn., pp. 317–356. Artech House, Boston (2006)
Zhang, G.Z., Huang, K.S., Jiang, W.L., et al.: Emitter feature extract method based on signal envelope. Systems Engineering and Electronics 936(6), 795–797 (2006)
Zilberman, E.R., Pace, P.E.: Autonomous Time-Frequency morphological feature extraction algorithm for LPI radar modulation classification. In: ICIP, pp. 2321–2324 (2006)
Zhu, M., Pu, Y.W., Wang, J.H., et al.: A novel feature extraction approach for radar emitter signals. In: Proceedings of ICSP 2008, pp. 2338–2341 (2008)
Zhang, G.X., Rong, H.N.: Entropy feature extraction approach for radar emitter signals. In: Proceedings of the 2004 International Conference on Intelligent Mechatronics and Automation, Chengdu, China, pp. 621–625 (August 2004)
Pincus, S.M.: Approximate entropy as a measure of system complexity. Pro. Natl. Acad. Sci. 88, 2297–2301 (1991)
Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278(6), H2039–H2049 (2000)
Liu, G.Z., Zhao, G.R., Huang, B.Y., et al.: Use of refined sample entropy and heart rate variability to assess the effects of wearable respiratory biofeedback. Bulletin of Advanced Technology Research 4(9), 48–53 (2010)
Zhao, H., Wang, G.J., Hu, L., et al.: Voice activity detection based on sample entropy in car environments. Journal of Computer Research and Development 48(3), 471–476 (2011)
Parkash, O., Sharma, P.K., Mahajan, R.: New measures of weighted fuzzy entropy and their applications for the study of maximum weighted fuzzy entropy principle. Information Sciences 178, 2389–2395 (2008)
Fan, J.L., Zhao, F.: A generalized fuzzy entropy thresholding segmentation method based on the sugeno complement operator. Journal of Electronics & Information Technology 30(8), 1865–1868 (2008)
Zhang, G.X., Rong, H.N., Jin, W.D.: Application of support vector machine to radar Emitter signal recognition. Journal of Southwest JiaoTong University 41(1), 25–30 (2006)
Chen, T.W., Jin, W.D., Li, J.: Feature extraction using surrounding-line integral bispectrum for radar emitter signal. In: ICICS (2009)
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Wang, S., Zhang, D., Bi, D., Yong, X., Li, C. (2012). Radar Emitter Signal Recognition Based on Sample Entropy and Fuzzy Entropy. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_81
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DOI: https://doi.org/10.1007/978-3-642-31919-8_81
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