Optimal multi-dimensional fusion model for sensor allocation and accuracy assessment

I We describe a multi-dimensional model for fusion of activity based intelligence (ABI) hypothesis-driven evidence through optimal sensor management. We determine decision-making strategies based upon ability to perform data mining and pattern discovery utilizing open source, actionable information to prepare for specific events or situations from multiple information sources. Our solution is based on an analytical framework using game theory to support ingestion of data sources (evidence); integration of analytical algorithms for feature extraction, crowd source analysis, open source data mining, trends, and pattern analysis and linear game theory optimization to support multiple hypothesis analysis. This solution may also save money by offering a Pareto efficient, repeatable process for resource management. We combine operations research methods and remote sensing for decision-making with several possible actions, state of world, and a mixed pro bability metric. Our tool allows for calculating optimal strategies, provides greater knowledge about remote sensing access times and increases likelihood of a decision-maker making best decision. We fuse evidence using Dempster's Rule and Nash Equilibrium (NE) for allocation of demands by sensor modality. We discuss a method for calculating optimal detector to determine accuracy of resource allocation. By calculating all NE possibilities per period, optimization of sensor allocation is achieved for overall higher system efficiency. We model impact of decision-making on accuracy by adding more dimensions to decision-making process as sensitivity analysis. Future work is to implement the design on a distributed processing platform to support real-world-sized scenarios and simulations.