Robust data alignment based on information theory and its applications in road following situation

This paper presents a new approach to data fusion for automatic target recognition, surveillance, and tracking research areas growing in an intelligent transportation system. Robust data alignment (RDA), finding relational maps among a sequence of invariant feature data sets, is one of the key requirements for a successful data fusion. To achieve RDA for a multi-modal data fusion, we construct a cost criterion based on information theory and solve an optimization problem with a mixed search strategy combining the Nelder-Mead simplex and random search method. We evaluate the cost criterion and search strategy by a numerical stability test, and demonstrate experimental results on a video sequence collected from an unmanned aerial vehicle (UAV). The results indicate the potential of aerial monitoring and tracking system built upon our information-theoretic RDA