Neural network directed Bayes decision rule for moving target classification Yu, Xi ; Azimi-Sadjadi, Mahmood R. "This work was funded by the Optoelectronic Computing Systems (OCS) Center at Colorado State University, under NSF/REC Grant 9485502." 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. Colorado State University. Libraries 2000 text ; image application/pdf ECEmra00025.pdf FACFECEN100437ARTI eng c2000 IEEE
Neural network directed Bayes decision rule for moving target classification
Yu, Xi ; Azimi-Sadjadi, Mahmood R.
"This work was funded by the Optoelectronic Computing Systems (OCS) Center at Colorado State University, under NSF/REC Grant 9485502."
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.
Colorado State University. Libraries
2000
text ; image
application/pdf
ECEmra00025.pdf
FACFECEN100437ARTI
eng
c2000 IEEE