Sensor Task Optimization and Real-time Management (STORM)

"The emergence of the digital battlespace and the emphasis on military joint operations provide access to a plethora of information resources and collection assets. Sensor management, in general, and the capability to automate scheduling and dynamicallyre-task sensor assets in light of changing operational requirements and mission objectives, forms a key element to ensuring information dominance for our warfighters. Here, we propose a Sensor Task Optimization and Real-time Management (STORM) system forthe scheduling, coordination, and path planning of heterogeneous sensor assets and the dynamic adaptation and re-allocation of those assets in response to changing battlespace conditions. The system uses threat prediction to prioritize the search area anda geometric partitioning scheme to divide the area into smaller, more manageable parts. Our scheduling module uses these results to create an optimal search schedule for available sensor assets using an Ant Colony Optimization (ACO) algorithm, which is anagent-based approach that incorporates heterogeneous sensors and is extendable to new sensor technologies. We see considerable potential for this approach in enhancing Navy sensor management capabilities and in the rapidly growing commercial applicationsof scheduling and routing solutions. Commercial applications of the proposed approach to schedule optimization exist for a wide variety of domains including transportation, airways and ra