Multi-aspect target discrimination using hidden Markov models and neural networks Robinson, Marc ; Azimi-Sadjadi, Mahmood R. ; Salazar, Jaime "This work was supported by the Office of Naval Research Biosonar Program under Contract N00014-01-1-0307." This paper presents a new multi-aspect pattern classification method using hidden Markov models (HMMs). Models are defined for each class, with the probability found by each model determining class membership. Each HMM model is enhanced by the use of a multilayer perception (MLP) network to generate emission probabilities. This hybrid system uses the MLP to find the probability of a state for an unknown pattern and the HMM to model the process underlying the state transitions. A new batch gradient descent-based method is introduced for optimal estimation of the transition and emission probabilities. A prediction method in conjunction with HMM model is also presented that attempts to improve the computation of transition probabilities by using the previous states to predict the next state. This method exploits the correlation information between consecutive aspects. These algorithms are then implemented and benchmarked on a multi-aspect underwater target classification problem using a realistic sonar data set collected in different bottom conditions. Colorado State University. Libraries 2005 text ; image application/pdf ECEmra00011.pdf FACFECEN100491ARTI eng c2005 IEEE
Multi-aspect target discrimination using hidden Markov models and neural networks
Robinson, Marc ; Azimi-Sadjadi, Mahmood R. ; Salazar, Jaime
"This work was supported by the Office of Naval Research Biosonar Program under Contract N00014-01-1-0307."
This paper presents a new multi-aspect pattern classification method using hidden Markov models (HMMs). Models are defined for each class, with the probability found by each model determining class membership. Each HMM model is enhanced by the use of a multilayer perception (MLP) network to generate emission probabilities. This hybrid system uses the MLP to find the probability of a state for an unknown pattern and the HMM to model the process underlying the state transitions. A new batch gradient descent-based method is introduced for optimal estimation of the transition and emission probabilities. A prediction method in conjunction with HMM model is also presented that attempts to improve the computation of transition probabilities by using the previous states to predict the next state. This method exploits the correlation information between consecutive aspects. These algorithms are then implemented and benchmarked on a multi-aspect underwater target classification problem using a realistic sonar data set collected in different bottom conditions.
Colorado State University. Libraries
2005
text ; image
application/pdf
ECEmra00011.pdf
FACFECEN100491ARTI
eng
c2005 IEEE