<title>Hierarchical-based fusion strategy for unattended ground sensors</title>

This paper presents a technical approach for fusion of data from a distributed set of remote fusion nodes. Each remote fusion node contains a suite of unattended ground sensors (acoustic, magnetic, seismic, and environmental). A multiple-hypothesis fusion architecture is developed for fusing the output of the remote sensor outputs. Both target kinematic and target attribute data is processed for the purpose of localizing and classifying targets of interest. The higher-level fusion products of these remote fusion nodes (target state and classification) are then passed on to a central fusion node that also employs a multiple- hypothesis fusion architecture. With this hierarchical-based strategy, we illustrate how we obtain near-optimal fusion performance while maintaining a high degree of redundancy from potential loss of one or more of the remote fusion sites.