Mutual information based sensor tasking with applications to space situational awareness

This paper focuses on the problem of managing or tasking a network of sensors to accurately track a number of objects while using information theoretic sensor performance metrics. The mathematical formulation of optimally tasking a group of sensors using the mutual information as a sensor utility measure is discussed along with the relative merits of maximizing mutual information. The resulting sensor-tasking optimization problem is shown to be combinatorial in nature, for which the computational complexity increases with an increase in the number of objects as well as the number of sensors. Depending upon the number of objects and available sensors, appropriate suboptimal approximations are presented to alleviate the computational complexity of the tasking problem. The submodular property of the mutual information measure is used to provide guarantees on the optimality of different approximations. Numerical simulations involving tracking ground objects with moving unmanned aerial vehicles and tracking resident space objects with ground-based sensors are considered to show the efficacy of the developed methods.