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Proceedings Paper

Inclusion of noise in a maximum-likelihood classifier
Author(s): Jim P. Ballard
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Paper Abstract

For maximum likelihood and other parameter-based classifiers, it is wrong to assume that noise can be dealt with by removing the mean noise power from the combined signal and noise spectrum. Doing this takes no account of the variance of the noise power, leading to the assignation of very low probabilities to probable events and thus misclassification. Instead, the effect of the new noise level on the parameters of the probability density function should be calculated and these new parameters used in the probability calculations on the unadjusted signal and noise spectrum. Hence the effect of different noise levels may be robustly included in the classifier without the need to train the classifier at a number of different noise levels. This technique of adjusting the database of parameters is then compared to the standard method of manipulating the signal to be classified. This is done by comparing the noise adjustment algorithms' performance when they are included in a maximum-likelihood, radar range- profile ship classifier, which has 7 different classes. The performances of these algorithms are evaluated as a function of range and signal-to-noise ratio. The parameter-adjustment technique is shown to yield much better performance than the traditional signal-adjustment method.

Paper Details

Date Published: 18 September 1998
PDF: 7 pages
Proc. SPIE 3371, Automatic Target Recognition VIII, (18 September 1998); doi: 10.1117/12.323879
Show Author Affiliations
Jim P. Ballard, Defence Evaluation and Research Agency Malvern (United Kingdom)


Published in SPIE Proceedings Vol. 3371:
Automatic Target Recognition VIII
Firooz A. Sadjadi, Editor(s)

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