A neural network-based sequential Bayes classifier for moving target discrimination

In this paper, a new neural network-based sequential Bayes classifier is developed for target classification exploring the target´s dynamic behavior. The system consists of a feature extractor, a neural network directed conditional probability generator and a sequential Bayes classifier. The velocity and curvature sequences extracted from each trade are used as the primary features. 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 Bayes classifier to make the classification. The classification results are updated regressively whenever a new scan of data is received. Simulation results on both clean tracks and heavily cluttered infrared (IR) satellite images are presented to demonstrate the effectiveness of the proposed methods