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.

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Eric W. Frew
• Keywords: Active Sensing, IROS 2008, Probability Distribution, Robotics, Sample Points