Journal of Computers, Vol 6, No 9 (2011), 1976-1982, Sep 2011
doi:10.4304/jcp.6.9.1976-1982

A New Method of Detecting Multi-component LFM Signals Based on Blind Signal Processing

Qiang Guo, Yajun Li, Changhong Wang

Abstract


To effectively detect and recognize multicomponent 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 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 signals separtion and feature extraction was better.


Keywords


multi-component LFM emitter signals;time-frequency analysis; feature extraction;ICA

References


[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)

[2] Ashok Narayanan V, Prabhu K M M. The fractional fourier transform:theory, implementation and error analysis[J]. Elsevier Micorprocessors and Microsystems, Vol.27(2003),p.511-521
http://dx.doi.org/10.1016/S0141-9331(03)00113-3

[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),p.1-8
http://dx.doi.org/10.1016/S0129-0657(00)00002-8

[4] J.Herault and C.Jutten.Blind separation of sources, part: an adaptive algorithm based on neuro mimetic. Signal Processing, Vol.24(1)(1991),p.1-10.
http://dx.doi.org/10.1016/0165-1684(91)90079-X

[5] LIU Q S, LU H Q, MA S D, “A Non-parameter Bayesian Classifier for Face Recognition [J], ”Journal of Electronics (China), Vol.20(5)(2003),p.362 -370.
http://dx.doi.org/10.1007/s11767-003-0046-2

[6] Shimizu S., Hyvarinen A., Kano Y. A generalized least squares approach to blind separation of sources which have variance dependencies[J].Statistical Signal Processing, IEEE/SP 13th Workshop on(2005),p.1080-1083

[7] Tachibana K., Saruwatari H., Mori Y. Efficient Blind Source Separation Combining Closed-Form Second-Order ICA and Nonclosed-Form Higher-Order ICA. IEEE International Conference on Acoustics,Speech and Signal Processing. ICASSP 2007. Vol. 1(2007), p.I-45-I-48

[8] Chee-Ming Ting, Salleh S.-H., Zainuddin Z.Z. Spectral Estimation of Nonstationary EEG Using Particle Filtering With Application to Event-Related Desynchronization (ERD) [J]. IEEE Transactions on Biomedical Engineering. Vol. 58(2011) p.321-331
http://dx.doi.org/10.1109/TBME.2010.2088396
PMid:21257361

[9] Zou Hong-xing, LU Xu-guang, DAI Qiong-hai. Nonexistence of cross-term free time-frequency distribution with concentration of Wigner-ville distribution, Vol.3(2002)

[10] Yuan junquan,Sun minqi,Sun xiaoxu, “LFM signal parameters estimation method based on Wigner Hough Transform”,Aerospace Electronic Countermeasures, Vol.6 (2004).

[11] Solvang H.K.,Nagahara Y.,Araki S. Frequency-Domain Pearson Distribution Approach for Independent Component Analysis (FD-Pearson-ICA) in Blind Source Separation[J]. IEEE Transactions on Audio,Speech,and Language Processing . Vol.17,No.4(2009),p.:639-648
http://dx.doi.org/10.1109/TASL.2008.2011527

[12] Liu ju, He zhenya, Zhang xianda. Blind Source Separation and Blind Deconvolution. Electronics Journal, Vol.30(4) (2002),p.570-576

[13] Li xiaoju,Zhu xiaolong,Zhang xianda. Blind source separation classification and prospects. Journal of Xi'an University of Electronic Science and Technology, Vol.31(3) (2004),p.399–404

[14] Zhang xianda, Bao zheng. Blind signal separation. E-Journal. Vol.29(12) (2001),p.1766-1771.

[15] Zou hong. Time-frequency analysis of multi-component LFM signals [D]. Xi'an Electronic Science and Technology University, 2000.

[16] Liu Jiancheng, Wang Xuesong, Xiao Shunping, et a1. Radial acceleration estimation based on Wigner-Hough transform[J]. Acta Electronica Sinica, Vol.33(12) (2005),p.2236-2238.


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