Information Fusion and Performance Modeling with Distributed Sensor Networks

Abstract : The focus of this project is to develop and demonstrate the fusion methodologies to enhance the ability to integrate multi-source information and to assess fusion performance for numerous applications such as situational awareness, surveillance, and tracking. The focus of the Year 1 effort was on developing a solid theoretical foundation and on developing autonomous and efficient information fusion algorithms with distributed sensors. The focus of Year 2 effort was to develop a set of fusion performance modeling methodologies based on explicit links of spatial and temporal relationships between target features and sensor observations. We have accomplished the goals. Specifically, we developed a set of scalable fusion algorithms and the corresponding theoretical performance analysis in a dynamic sensor network environment. We have also developed a framework for quantifying the classification performance of a set of sensors with varying qualities based on local confusion matrix and global confusion matrix using Bayesian network model. In addition, we have developed a software tool based on UnBBayes open source environment to test the performance modeling. The resulting methodology has significant potential for applications in high level fusion and situational assessment.

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