Title :
Multi-aspect target discrimination using hidden Markov models and neural networks
Author :
Robinson, Marc ; Azimi-Sadjadi, Mahmood R. ; Salazar, Jaime
Author_Institution :
Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA
fDate :
3/1/2005 12:00:00 AM
Abstract :
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.
Keywords :
correlation methods; gradient methods; hidden Markov models; multilayer perceptrons; pattern classification; batch gradient descent method; correlation information; hidden Markov model; multiaspect pattern classification; multiaspect underwater target classification; multilayer perception network; sonar data set; Decision making; Hidden Markov models; Neural networks; Nonhomogeneous media; Pattern classification; Prediction methods; Predictive models; Robustness; Shape; Sonar; Hidden Markov models (HMMs); multi-aspect pattern classification; neural networks; prediction; underwater target classification; Discriminant Analysis; Markov Chains; Neural Networks (Computer);
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2004.841805