Journal of Computers, Vol 7, No 6 (2012), 1413-1420, Jun 2012
doi:10.4304/jcp.7.6.1413-1420

Radar Emitter Signal Recognition Based on EMD and Neural Network

Bin Zhu, Wei-dong Jin

Abstract


Radar emitter signal (RES) recognition is the important content in radar reconnaissance and signal processing. In order to study the problem of RES recognition, and to improve the RES recognition rate of the electronic warfare equipment, the empirical mode decomposition (EMD) theory and wavelet packet (WP) are introduced into RES feature extraction. A new RES recognition method is proposed based on WP, EMD and neural network (NN). It uses wavelet packet to finish decomposition, de-noising and reconstruction of the RES. Then obtain the intrinsic mode function (IMF) through EMD, which can embody the characteristics of the RES. The energy of each IMF are calculated and normalized, which would be regarded as the feature vector. By constructing back propagation neural network (BPNN) classifier and redial basis function neural network (RBFNN) classifier, it realizes the RES recognition finally. Experiment results show that the RES recognition method based on WP, EMD and NN is an effective recognition method, which can achieve satisfying correct recognition rate in a larger signal to noise ratio, and has certain reference value in follow-up in-depth study.


Keywords


radar emitter;signal recognition;empirical mode decomposition;neural network;wavelet packet

References


 

[1] JIN Wei-dong, ZHANG Ge-xiang, HU Lai-zhao, “Radar emitter signal recognition using wavelet packet transform and support vector machines”, Journal of Southwest Jiaotong University, Vol. 14, pp. 15-22, Jan 2006.

[2] YU Zhi-bin, JIN Wei-dong, CHEN Chun-xia, “Radar emitter signal recognition based on WRFCCF”, Journal of Southwest Jiaotong University, Vol. 45, pp. 290-295, April 2010.

[3] CHEN Tao-wei1, ZHU Ming, CHEN Zhen-xing, “Feature extraction of radar emitter signals based on wavelet transform”, Computer Engineering and Applications, Vol. 46, pp. 245-248, Jun 2010.

[4] CHENG Ji-xiang, Zhang Ge-xiang, Li Zhi-dan, “A novel specific emitter identification method based on time-frequency atom approach”, Aerospace Electronic Warfare, Vol. 27, pp. 54-57, Jan 2011.

[5] ZHANG Ge-xiang, RONG Hai-na, JIN Wei-dong, “Radar emitter signal recognition based on wavelet packet transform and feature selection”, Journal of Circuits and Systems, Vol.11, pp. 45-49, Aug 2006.

[6] N.E. Huang, Z. Shen, S.R. Long, M.L. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung, H.H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”, Proceedings of the Royal Society of London Series A 454, pp.903-995, 1998.
http://dx.doi.org/10.1098/rspa.1998.0193

[7] H. Ding, Z.Y. Huang, Z.H. Song, Y. Yan, “Hilbert-Huang transform based signal analysis for the characterization of gas-liquid two phase flow”, Flow Measurement and Instrumentation, Vol.18, pp.37-46, 2007
http://dx.doi.org/10.1016/j.flowmeasinst.2006.12.004

[8] Z.K. Peng, Peter W. Tse, F.L. Chu, “An improved Hilbert-Huang transform and its application in vibration signal analysis”, Journal of Sound and Vibration, Vol. 286, pp. 187-205, 2005.
http://dx.doi.org/10.1016/j.jsv.2004.10.005

[9] S. Kizhner, K. Blank, T. Flatley, N.E. Huang, D. Petrick, P. Hestnes, “On certain theoretical developments underlying the Hilbert-Huang transform”, Proceedings of IEEE Aerospace Conference, Big Sky, MT, USA, March 2006.

[10] Y. Kopsinis, S. McLaughlin, “Enhanced empirical mode decomposition using a novel sifting-based interpolation points detection”, Proceedings of IEEE Statistical Signal Processing, Madison, WI, USA, pp. 725-729, 2007.

[11] SONG Chun-yun, XU Jian-min, ZHAN Yi, “A method for specific emitter identification based on empirical mode decomposition”. IEEE International Conference on Wireless Communications, Networking and Information Security, Vol. 01, pp. 54-57, 2010.
http://dx.doi.org/10.1109/WCINS.2010.5541885

[12] Ying J G, WAN X Y, “Analysis of right and left motor imagery based on empirical mode decomposition”, Progress in Biomedical Engineering, Vol.30, pp. 125-130, 2009.

[13] QU Cong-shan, LU Ting-zhen, “A modiļ¬ed empirical mode decomposition method with applications to signal de-noising”, ACTA AUTOMATICA SINICA, Vol. 36, pp. 67-73, 2010.
http://dx.doi.org/10.3724/SP.J.1004.2010.00067

[14] Donoho D L, “De-noising by Soft-shareholding”, IEEE Trans on IT, Vol.41, pp. 613-627, 1995.
http://dx.doi.org/10.1109/18.382009

[15] Zhang X, Zhang J M, “Roller bearing fault diagnosis based on wavelet packet and EMD”, Microcomputer Information, Vol.24, pp. 158-159, 2008.

[16] Zhang S Q, Shang guan H L, etc, “Study on the extraction method of characteristic parameters of respiration signals based on intrinsic mode energy ratio”, Chinese Journal of Scientific Instrument, Vol.31, pp. 1706-1711, 2010.

[17] D Graupe, “Principles of artificial neural networks”, Singapore: World Scientific Press, 1997.

[18] B M. Wilamowski, R C. Jaeger, “Implementation of RBF type networks by MLP networks”, Proceedings of the IEEE International Conference on Neural Networks, pp. 1670-1675, 1996.

[19] FANG Shi-liang, LU Ji-ren, “The application of a combined basis neural network in the underwater acoustic target classification and recognition”, Technical Acoustics, Vol. 17, pp. 54-62, 1998.

[20] DING S Q, XIANG C, “From multilayer perceptrons to radial basis function networks: a comparative study”, Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Vol. 6, pp. 69-74, 2004.


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