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The importance of the optimal Sensor Resource Management (SRM) problem is growing. The number of Radar, EO/IR, Overhead Persistent InfraRed (OPIR), and other sensors with best capabilities, is limited in the stressing tasking environment relative to sensing needs. Sensor assets differ significantly in number, location, and capability over time. To determine on which object a sensor should collect measurements during the next observation period k, the known algorithms favor the object with the expected measurements that would result in the largest gain in relative information. We propose a new tasking paradigm OPTIMA for sensors that goes beyond information gain. It includes Sensor Resource Analyzer, and the Sensor Tasking Algorithm (Tasker). The Tasker maintains timing constraints, resolution, and geometric differences between sensors, relative to the tasking requirements on track quality and the measurements of object characterization quality. The Tasker does this using the computational intelligence approach of multi-objective optimization, which involves evolutionary methods.
Kovalerchuk B., Perlovsky L. (2016) Sensor Resource Management: Intelligent Multi-objective Modularized Optimization Methodology and Models. In: Abielmona R., Falcon R., Zincir-Heywood N., Abbass H. (eds) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-319-26450-9_25
Recent Advances in Computational Intelligence in Defense and Security
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