International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.3, No.3, Jun. 2011

Cyclic Spectral Features Extracting of Complex Modulation Signal Based on ACP Method

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ZHANG Xin,LIU Feng,ZHENG Peng,Wang Ze-Zhong

Index Terms

Complex modulation signal; cyclic spectral analysis; feature extraction


Based on averaged cyclic periodogram cyclic spectral density estimating method(ACP), the cyclic spectral features of complex modulated signals are studied and the correspondence with signal parameters is investigated. The feature extraction methods without prior knowledge are developed. Firstly, the expression of complex modulated signals is described and the relationship between signal parameters is given; Secondly, the cyclic spectral features of signals are analyzed using ACP cyclic spectral density estimating method, the features correspondence with signal parameters is obtained; Based on the above, a method for parameter extracting based on cyclic spectral features is proposed. The normalized RMS error (NRMSE) of frank coded and Costas coded signals parameter extraction are measured to verify the validity of the method.

Cite This Paper

ZHANG Xin, LIU Feng, ZHENG Peng, Wang Ze-Zhong, "Cyclic Spectral Features Extracting of Complex Modulation Signal Based on ACP Method", International Journal of Information Technology and Computer Science(IJITCS), vol.3, no.3, pp.50-56, 2011. DOI: 10.5815/ijitcs.2011.03.08


[1]P. E. Pace, “Detecting and Classifying Low Probability of Intercept Radar”, Norwood, MA: Horizon House Artech, 2004.

[2]P. D. Sutton, K. E. Nolan and L. E. Doyle, “Cyclostationary Signatures in Practical Cognitive Radio Applications”, Selected Areas in Communications, IEEE Journal on, vol.26, pp. 13-24, 2008.

[3]Sanmartin-Jara, Burgos-Garcia,M, and Retamosa-Sanchez, Radar sensor using low probability of interception SS-FH signals.[J].IEEE Aerospce and Electronics Magazine, April 2000. pp 23-28

[4]Akay O,Erozden E. Use of fractional autocorrelation in efficient detection of pulse compression radar signals[C], IEEE First International Symposium on Contrlo, Communications and Signal Processing, 2004:33-36

[5]N Levanon L U. Two-Valued frequency-coded waveforms with favorable periodic autocorrelation[J]. IEEE Trans on AES. 2006,42(1):p237-p248.

[6]N Levanon, E Mozeson, Radar Signals[M],A John Wiley & Sons,INC., Hoboken,New Jersey:100-168,2004.

[7]Jennison B K.Detection of polyphase pulse compression wave forms using the Radonambiguity transform[J].IEEE Trans.on Aerospce and Elec.Sys,2003,39:225-343

[8]Milne P R, Pace P E. Wigner distribution detection and analysis of FMCW and P4 polyphase LPI waveforms[C]. Proc.IEEE International Conf.on Acoustics, Speech and Signal Processing, 2002:3944-3947

[9]W. Jiandong, C. Tongwen and H. Biao, “Cyclo-period estimation for discrete-time cyclo-stationary signals”, Signal Processing, IEEE Transactions on, vol.54, pp. 83-94, 2006.

[10]K.-S. Lii and M. Rosenblatt, “Estimation for almost periodic pro-cesses,” Ann. Statist., vol. 34, no. 3, pp. 1115–1139, 2006.

[11]T. O. Gulum, P. E. Pace, “Extraction of Polyphase Radra Modulation Parameters Using a Wigner-Ville Distribution-Radon Transform”, IEEE International Conf. on Acoustics,Speech and Signal Processing, Las Vegas, NV, March 2008

[12]Gardner, W. A., “Statistical Spectral Analysis: A Nonprobabilistic Theory”, Prentice-Hall, Englewood Cliffs, NJ, 1987.

[13]R. S. Roberts, W. A. Brown and Jr. H. H. Loomis, "Computationally efficient algorithms for cyclic spectral analysis," Signal Processing Magazine, IEEE, vol.8, pp. 38-49, 1991.

[14]Gardner, W.A, “Signal interception: A unifying theoretical framework for feature detection”, IEEE Trans. on Communications, vol. 36, No. 8, pp. 897-906, Aug. 1988.

[15]Antonio F. Lima, Jr. “Analysis of low probability of intercept radar signals using cyclostationary processing”, Naval Postgraduate School Master’s thesis, Sept.2002.

[16]T. O. Gulum, “Autonomous Nonlinear Classification of LPI Radar Signal Modulations,” Naval Postgraduate School Masters Thesis, Sept. 2007.

[17]Dobre.O.A. Abdi.A, Bar-Ness.Y. Su, W. “Survey of automatic modulation classification techniques: classical approaches and new trends”, Communications, lET,volume 1, pp. 137 - 156, (2007).

[18]A. V. Dandawate and G. B. Giannakis, “Asymptotic Theory of Mixed Time Averages and kth Order CyclicMoments and Cumulant Statistics,” IEEE Trans. Inform. Theory, vol. 41, pp. 216–232, January 1995.

[19]Antonio Napolitano, “Discrete-Time Estimation of Second-Order Statistics of Generalized Almost-Cyclostationary Processes”, IEEE Trans on signal processing, vol. 57, No. 5, May 2009

[20]A. Napolitano, "On the spectral correlation measurement of non-stationary stochastic processes," in Proc. 2001 Signals, Systems and Computers, 2001. Conference Record of the Thirty-Fifth Asilomar Conference on, pp. 898-902.