Approximating information content for active sensing tasks using the unscented transform

This paper presents an approach to approximate information content for active sensing tasks. The unscented transform is used to represent probability distributions by a set of representative sample points that capture the first and second moments of the distribution. Using these sample points, the effects of nonlinear operators on a probability distribution of active sensing costs can be approximated. Simulation results validate the approximation for bearings-only geolocalization of a stationary target and tracking of an uncertain moving target.