| 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 |