Adaptive Mixture-Based Neural Network Approach for Higher-Level Fusion and Automated Behavior Monitoring

A novel adaptive mixture-based neural network is presented for exploiting track data to learn normal patterns of motion behavior and detect deviations from normalcy. We have extended our prior approach by introducing multidimensional probability density components to represent class density using an adaptive mixture of such components. The number of components in the adaptive mixture algorithm, as well as the values of the parameters of the density components, is estimated from the data. The network utilizes a recursive version of the Expectation Maximization (EM) algorithm to minimize the Kullback-Leibler information metric by means of stochastic approximation combined with a rule for creation of new components. Learning occurs incrementally in order to allow the system to take advantage of increasing amounts of data without having to take the system offline periodically to update models. Continuous incremental learning enables the models of normal behavior to adapt well to evolving situations while maintaining high levels of performance. In addition, the adaptive mixtures neural network classifies streaming track data as normal or deviant. These capabilities contribute to higher-level fusion situational awareness and assessment objectives by enabling a shift of operator focus from sensor monitoring and activity detection to assessment and response. Our overall motion pattern learning approach learns behavioral patterns at a variety of conceptual, spatial, and temporal levels to reduce massive amounts of track data to a rich set of information regarding operator field of regard that supports rapid decision-making and timely response initiation.