A New Kind of FRFT Analysis Method for Multi-Component LFM Signals

Article Preview

Abstract:

To effectively detect and recognize multi-component Linear Frequency-Modulated (LFM) emitter signals, a multi-component LFM emitter signal analysis method based on the complex Independent Component Analysis(ICA) which was combined with the Fractional Fourier Transform(FRFT) was proposed. The idea which was adopted to this method was the time-domain separation and then time-frequency analysis, and in the low SNR cases, the problem which is generally plagued by noised of feature extraction of multi-component LFM signal based on FRFT is overcame. Compared to the traditional method of time-frequency analysis, the computer simulation results show that the proposed method for the multi-component LFM signal separation and feature extraction was better.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 204-210)

Pages:

973-978

Citation:

Online since:

February 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Liu feng, Sun dapeng, Huang yu, Tao ran, Wang yue, in : Multi-component LFM signal feature extraction based on improved Wigner-Hough transform, Journal of Beijing Technology University. (2008. 10).

DOI: 10.1109/wicom.2008.462

Google Scholar

[2] C. W. Therrien 1992, Discrete Random Signals and Statistical Signal Processing (Prentice -Hall).

Google Scholar

[3] Ella Blngham and Aapo Hyvarinen. A Fast Fixed-Point Algorithm For Independent Component Analysis Of Complex Valued Signals,. International Journal of Neural Systems, Vol. 10, No. 1(February, 2000), pp.1-8.

DOI: 10.1142/s0129065700000028

Google Scholar

[4] J. Herault and C. Jutten. Blind separation of sources, part: an adaptive algorithm based on neuro mimetic. Signal Processing, Vol. 24(1)(1991), pp.1-10.

DOI: 10.1016/0165-1684(91)90079-x

Google Scholar

[5] LIU Q S , LU H Q , MA S D, A Non2parameter Bayesian Classifier for Face Recognition [J] , Journal of Electronics (China), Vol. 20(5)(2003), pp.362-370.

Google Scholar

[6] Jutten C , Herault J . Blind separation of sources , Part I : An adaptive algorithm based on neuromimetic architecture[J]. Signal Processing, Vol. 24(1) (1991), p.1–10.

DOI: 10.1016/0165-1684(91)90079-x

Google Scholar

[7] P. Comon. Independent component analysis-a new concept?, Signal Processing , Vol. 36 (1994), pp.287-314.

DOI: 10.1016/0165-1684(94)90029-9

Google Scholar

[8] Xiao xianci. Modern Spectral Estimation:Principles and Applications [M]. Harbin:Harbin Institute of Technology Press,(1991).

Google Scholar