Abstract
A multitarget passive recognition and location method which fuses SVM and blind signal processing technique is proposed in this paper. Its characters are: Sampling data via multitarget information receiving array at first; And then getting separated signal and matrix by blind signal separation (BSS) to these data; Completing classification of each separated signal by using decision tree support vector machine (SVM) multitarget recognition process to the separated signal; Obtaining direction information of each signal by blind deconvolution location algorithm based on array model to the separated matrix at the same time; Finally, realizing target recognition and location by synthesizing targets information of the classification and direction. This paper studies technique principle of this method, gives a detailed implement step and proves its validity by multitarget recognition and location experiment of measured ship-radiated noise.
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References
Mitianoudis, N., Davies, M.E.: Audio source separation of convolutive mixtures. Speech and Audio Processing 11(5), 489–497 (2003)
Eriksson, J., Koivunen, V.: Complex-valued ICA using second order statistics. In: Proceedings of the 2004 IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing 2004, pp. 183–191 (2004)
Kopriva, I.: Blind signal deconvolution as an instantaneous blind separation of statistically dependent sources. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds.) ICA 2007. LNCS, vol. 4666, pp. 504–511. Springer, Heidelberg (2007)
Zhang, K., Chan, L.-W.: Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion. Neurocomputing 73(13-15), 2580–2588 (2010)
Douglas, S.C., Sawada, H., Makino, S.: Natural gradient multichannel blind deconvolution and speech separation using causal FIR filters. IEEE Transactions on Speech and Audio Processing 13(1), 92–104 (2005)
Xu, T., He, D.-K.: Theory of hypersphere multiclass SVM Kongzhi Lilun Yu Yinyong. Control Theory and Applications 26(11), 1293–1297 (2009)
Cherkassky, V., Ma, Y.: Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17(1), 113–126 (2004)
Gao, H., Liu, W.: An improved SVM classifier ICIC Express Letters 3(4), 1001–1005 (2009)
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© 2011 Springer-Verlag Berlin Heidelberg
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Bai, J., Wang, H., Shen, X., Chen, Z. (2011). A Multitarget Passive Recognition and Location Method Fusing SVM and BSS. In: Wan, X. (eds) Electrical Power Systems and Computers. Lecture Notes in Electrical Engineering, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21747-0_10
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DOI: https://doi.org/10.1007/978-3-642-21747-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21746-3
Online ISBN: 978-3-642-21747-0
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