The generalised sequentially rejective Bonferroni test applied to non-stationary, random signal classification

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The generalised sequentially rejective Bonferroni test applied to non-stationary, random signal classification

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dc.contributor.author Roberts, G
dc.contributor.author Zoubir, A.M.
dc.contributor.author Boashash, B
dc.date.accessioned 2012-10-18T11:15:43Z
dc.date.available 2012-10-18T11:15:43Z
dc.date.issued 1996
dc.identifier.citation G. Roberts, A. M. Zoubir, and B. Boashash, "The generalised sequentially rejective Bonferroni test applied to non-stationary, random signal classification," in Proc. of Systems, Man, and Cybernetics, 1996., IEEE International Conference on, 1996, pp. 2728-2732 vol.4. (doi: 10.1109/icsmc.1996.561371) en_US
dc.identifier.other doi: 10.1109/icsmc.1996.561371
dc.identifier.uri http://hdl.handle.net/10576/10875
dc.description This paper presents a new method for the classification of non-stationary Gaussian signals by combining time frequency analysis with multiple hypothesis testing for classes of signals that are inseparable in either the time or the frequency domain alone. (Additional relevant material and details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated). en_US
dc.description.abstract We present a new non-stationary signal classification algorithm based on a time-frequency distribution and multiple hypothesis testing. The time-frequency distribution is used to construct a time-dependent quadratic discriminant function. At selected points in time we evaluate the discriminant function and form a set of statistics which are used to test multiple hypotheses. We show that the statistics are a linear combinations of chi square random variables with constant coefficients and hence are not normally distributed. The multiple hypotheses are treated simultaneously using the generalised sequentially rejective Bonferroni test to control the probability of incorrect classification of one class. Finally, we show the results of classifying time-varying AR( 1) processes which have identical expected instantaneous power and power spectral densities but different time-frequency representations. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject chi square random variables en_US
dc.subject discriminant function en_US
dc.subject generalised sequentially rejective Bonferroni test en_US
dc.subject multiple hypothesis testing en_US
dc.subject onstationary random signal classification en_US
dc.subject time-dependent quadratic discriminant function en_US
dc.subject time-frequency distribution en_US
dc.subject time-varying AR processes en_US
dc.title The generalised sequentially rejective Bonferroni test applied to non-stationary, random signal classification en_US
dc.type Article en_US

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