Rigorous sensor resource management: Methodology and evolutionary optimization

The number of platforms and sensors with the best capabilities often is limited in the stressing tasking environment relative to the sensing needs. This is the case for Overhead Persistent InfraRed (OPIR) sensors. Sensor assets differ significantly in number, location, and capability over time. Planning for engagements prior to actual tasking sensors involves these factors. This paper proposes a new sensor tasking methodology and optimization models for both long-time planning and real-time Sensor Resource Management (SRM). It is based on the rigorous and adaptive mathematical formulation of the problem and the use of Computational Intelligence techniques such as genetic algorithms and dynamic logic to find a solution.