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Teams of manned and unmanned active sensors can provide tactical military units, search and rescue teams, and emergency response units with timely information; however, limited numbers of these systems mean their tasking must be prioritized, and the information they provide needs to be synthesized to avoid overwhelming users. Automated methods can fuse a priori and real-time information to provide decision-makers with time- critical situational awareness and a basis for search prioritization and route planning. Previous work has shown how expected entropic information gain can be used as a measure of utility in motion planning, though in multi- target search scenarios not all information is equally valuable. This research investigates generating certainty grids for dynamic search prioritization using a time-dependent cell valuation that incorporates entropy as well as threat- and geography-specific importance of information relative to the mission. We compare two different approaches to calculating posterior probability and entropy: a Bayesian log odds method based on prior works on obstacle avoidance; and a Dempster-Shafer Theory approach using a plausibility measure. The resulting weighted certainty grid map is provided for dynamic search. We then demonstrate how this adaptive, integrated situational awareness approach performs in different simulated, small unit tactical scenarios.
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