<title>Inclusion of noise in a maximum-likelihood classifier</title>

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.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.