Dynamic trees for sensor fusion

The dynamic tree (DT) graphical model is a popular analytical framework for image segmentation and object classification tasks. A DT is a useful model in this context because its hierarchical property encodes information in multiple scales and its flexible structure fits complex region boundaries better than rigid quadtree structures such as tree-structured Bayesian networks. This paper proposes a novel framework for data fusion by using a DT model to fuse measurements from multiple sensing platforms into a non-redundant representation. The structural flexibility of the DT will be used to combine common information across different sensor measurements of simulated objects of interest. The appropriate structure of the DT and its parameters for the data fusion application are presented and discussed along with fusion results from a simulated sonar survey mission.