Non-stationary, narrowband Gaussian signal discrimination in time-frequency space

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Non-stationary, narrowband Gaussian signal discrimination in time-frequency space

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dc.contributor.author Roberts, G
dc.contributor.author Zoubir, A.M.
dc.contributor.author Boashash, B
dc.date.accessioned 2012-06-18T14:27:02Z
dc.date.available 2012-06-18T14:27:02Z
dc.date.issued 1997
dc.identifier.citation G. Roberts, A.M. Zoubir and B. Boashash, “Non-stationary, narrowband Gaussian signal discrimination in time-frequency space,” in Digital Signal Processing for Communication Systems, T. Wysocki, editor, pp. 159-166, Kluwer Academic Publishers, 1997 en_US
dc.identifier.uri http://hdl.handle.net/10576/10851
dc.description This paper describes a new method for the classification of non-stationary Gaussian signals by combining time-frequency analysis with multiple hypothesis testing. (Additional 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).
dc.description.abstract The problem of signal classification can be divided into three consecutive sub-problems: detection of the presence of a signal; segmentation to determine the time interval of the signal; and classification of the signal into one of a finite number of classes. We assume that a signal is observed and its time interval is known and focus on the classification problem. In this paper we extend a frequency domain classifier for stationary signals [8] to a time-frequency classifier for non-stationary signals. The motivation for this extension is straightforward: the classical technique is only optimal (in the sense of minimising the probability of misclassifying an observation of one kind for a fixed misclassification rate of the other kind) if the signal is stationary. This leads us to consider a technique that does not require the signal to be stationary. In particular, we introduce a time-varying quadratic discriminant function using the spectrogram. We apply the generalised sequentially rejective Bonferroni test to the mUltiple hypotheses that can be constructed at different points in time from this discriminant function. Other classification techniques have been suggested recently using time-frequency distributions (TFD). In [10] the authors extended the log-spectral distance to the time-frequency case and in the authors proposed a technique based on the cross Wigner-Ville distribution. In the sequel we will discuss how our method deviates from the existing solutions. en_US
dc.language.iso en en_US
dc.publisher Kluwer Academic Publishers en_US
dc.subject signal segmentation
dc.subject signal detection
dc.subject time-frequency classifier
dc.subject non-stationary signals
dc.subject discriminant function
dc.subject time-frequency distributions
dc.subject log-spectral distance
dc.subject cross-Wigner-Ville Distribution
dc.subject time-frequency discrimination
dc.subject chi square random variables
dc.subject short-time Fourier transform
dc.subject time-frequency analysis
dc.subject multiple hypothesis testing
dc.subject test statistics
dc.title Non-stationary, narrowband Gaussian signal discrimination in time-frequency space en_US
dc.type Book chapter en_US

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