Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
A Non Parametric Estimation Based Underwater Target Classifier
Binesh T, Supriya M H, P R Saseendran Pillai
Pages - 156 - 164     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
Cepstral Coefficients, Linear Prediction Coefficients, H Statistic, F Statistic
Underwater noise sources constitute a prominent class of input signal in most underwater signal processing systems. The problem of identification of noise sources in the ocean is of great importance because of its numerous practical applications. In this paper, a methodology is presented for the detection and identification of underwater targets and noise sources based on non parametric indicators. The proposed system utilizes Cepstral coefficient analysis and the Kruskal-Wallis H statistic along with other statistical indicators like F-test statistic for the effective detection and classification of noise sources in the ocean. Simulation results for typical underwater noise data and the set of identified underwater targets are also presented in this paper.
CITED BY (0)  
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 Lawrence R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”, Proceedings of the IEEE, 77(2):257-273, 1989
2 W. H. Kruskal and W. A. Wallis, “Use of ranks in one-criterion variance analysis,” Journal of American Statistics Association, 47 : 583-621, Dec.1952
3 Schwarz and X. Rodet “Spectral Envelope estimation and representation for sound analysis- synthesis”, In Proceedings of International Computer Music Conference, ICMC 99, Beijing, 1999
4 Dirk K. de vries and Yves Chandon, “On the false positive rate of Statistical equipment comparisons based on the Kruskal-Wallis H-statistice”, IEEE Transactions on Semi conductor manufacturing, 20(3), 2007
5 Lawrence Rabiner and Biing-Hwang Juang , “Fundamentals of Speech Recognition”, NJ: PTR Prentice Hall, pp. 112-117 (1993)
6 Donghu Li, Azimi Sadjadi, M. R and Robinson, M, “Comparison of different Classification Algorithms for underwater target discrimination”, IEEE Transactions on Neural Networks, 15(1), 2004
7 M. Hollander & D.A. Wolfe, “Non parametric Statistical methods”, New York, Wiley, (1973).
8 J. R Deller, J. G. Proakis and F. H. L Hansen, “Discrete time processing of speech signals”, IEEE Press, p. 71, (2000)
9 L. R. Rabiner and B. H. Juang, “An Introduction to Hidden Markov Models”, IEEE ASSP Magazine, 3(1): pp. 4-16, 1986
Mr. Binesh T
Cochin University of Science and Technology,Kerala, India - India
Dr. Supriya M H
Cochin University of Science and Technology,Kerala, India - India
Dr. P R Saseendran Pillai
Cochin University of Science and Technology,Kerala, India - India