2 May 2012 Optimizing sensor placement using predictive geospatial analytics, the physical environment, and surveillance constraints
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Abstract
Military, police, and industrial surveillance operations could benefit from having sensors deployed in configurations that maximize collection capability. We describe a surveillance planning approach that optimizes sensor placements to collect information about targets of interest by using information from predictive geospatial analytics, the physical environment, and surveillance constraints. We designed a tool that accounts for multiple sensor aspects-collection footprints, groupings, and characteristics; multiple optimization objectives-surveillance requirements and predicted threats; and multiple constraints-sensing, physical environment (including terrain), and geographic surveillance constraints. The tool uses a discrete grid model to keep track of geographic sensing objectives and constraints, and from these, estimate probabilities for collection containment and detection. We devised an evolutionary algorithm and polynomial time approximation schemes (PTAS) to optimize the tool variables above to generate the positions and aspect for a network of sensors. We also designed algorithms to coordinate a mixture of sensors with different competing objectives, competing constraints, couplings, and proximity constraints.
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Greg Schmidt, Greg Schmidt, Brandon Witham, Brandon Witham, Jason Valore, Jason Valore, Ben Holland, Ben Holland, Jason Dalton, Jason Dalton, } "Optimizing sensor placement using predictive geospatial analytics, the physical environment, and surveillance constraints", Proc. SPIE 8396, Geospatial InfoFusion II, 83960A (2 May 2012); doi: 10.1117/12.921513; https://doi.org/10.1117/12.921513
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