Neural network directed Bayes decision rule for moving target classification
Date
2000
Authors
Yu, Xi, author
Azimi-Sadjadi, Mahmood R., author
IEEE, publisher
Journal Title
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Abstract
In this paper, a new neural network directed Bayes decision rule is developed for target classification exploiting the dynamic behavior of the target. The system consists of a feature extractor, a neural network directed conditional probability generator and a novel sequential Bayes classifier. The velocity and curvature sequences extracted from each track are used as the primary features. Similar to hidden Markov model (HMM) scheme, several hidden states are used to train the neural network, the output of which is the conditional probability of occurring the hidden states given the observations. These conditional probabilities are then used as the inputs to the sequential Bayes classifier to make the classification. The classification results are updated recursively whenever a new scan of data is received. Simulation results on multiscan images containing heavy clutter are presented to demonstrate the effectiveness of the proposed methods.
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Subject
Bayes methods
backpropagation
correlation methods
feature extraction
image classification
least mean squares methods
neural nets
object recognition
probability
radar clutter