dc.contributor.author |
Kartchner, DR |
|
dc.contributor.author |
Jayaweera, SK |
|
dc.contributor.editor |
Chathuranga, D |
|
dc.date.accessioned |
2022-09-02T04:42:14Z |
|
dc.date.available |
2022-09-02T04:42:14Z |
|
dc.date.issued |
2018-05 |
|
dc.identifier.citation |
D. R. Kartchner and S. K. Jayaweera, "A Modified Test Statistic for Maximum-Minimum Eigenvalue Detection Based on Asymptotic Distribution Thresholds," 2018 Moratuwa Engineering Research Conference (MERCon), 2018, pp. 25-30, doi: 10.1109/MERCon.2018.8421979. |
en_US |
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/18857 |
|
dc.description.abstract |
The ratio of the maximum and minimum eigenvalues
of the sample covariance matrix has been suggested as
a test statistic for signal detection in low-SNR regimes. The
threshold required to implement a Neyman-Pearson test on this
statistic is usually computed by estimating the distribution of
this eigenvalue ratio under the null hypothesis using results
from random matrix theory (RMT). However, in order to apply
asymptotic laws from RMT, the data matrix used to construct the
test statistic must have statistically independent columns, which
was not satisfied by the test statistics used in previously proposed
detectors. This paper forms a data matrix with independent
columns to compute the test statistic for maximum-minimum
eigenvalue (MME) detection and compares its performance to
that of the test statistic as currently defined in literature. The
comparison is made with both the semi-asymptotic threshold,
which uses the limiting distribution of the maximum eigenvalue
and the asymptotic constant to which the minimum eigenvalue
converges; as well as the limiting distribution-based threshold,
which uses the limiting distribution of the ratio of the maximum
and minimum eigenvalues. Simulations compare the expected
false alarm rate versus actual false alarm rate, as well as the
receiver operating characteristic (ROC) for the following three
cases: the two test statistics with the semi-asymptotic threshold,
the two test statistics with the limiting distribution threshold,
and the two thresholds in conjunction with the newly proposed
test statistic. Results demonstrate that the newly proposed test
statistic with the limiting distribution threshold is the only case
where the actual false alarm rate remains consistently below
the false alarm constraint set in the Neyman-Pearson test, while
the previous test statistics are almost completely unresponsive to
changes to the false alarm constraint. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/8421979 |
en_US |
dc.title |
A modified test statistic for maximum-minimum eigenvalue detection based on asymptotic distribution thresholds |
en_US |
dc.type |
Conference-Full-text |
en_US |
dc.identifier.faculty |
Engineering |
en_US |
dc.identifier.department |
Engineering Research Unit, University of Moratuwa |
en_US |
dc.identifier.year |
2018 |
en_US |
dc.identifier.conference |
2018 Moratuwa Engineering Research Conference (MERCon) |
en_US |
dc.identifier.place |
Moratuwa, Sri Lanka |
en_US |
dc.identifier.pgnos |
pp. 25-30 |
en_US |
dc.identifier.proceeding |
Proceedings of 2018 Moratuwa Engineering Research Conference (MERCon) |
en_US |
dc.identifier.doi |
10.1109/MERCon.2018.8421979 |
en_US |