Optimality analysis of sensor-target localization geometries

The problem of target localization involves estimating the position of a target from multiple and typically noisy measurements of the target position. It is well known that the relative sensor-target geometry can significantly affect the performance of any particular localization algorithm. The localization performance can be explicitly characterized by certain measures, for example, by the Cramer-Rao lower bound (which is equal to the inverse Fisher information matrix) on the estimator variance. In addition, the Cramer-Rao lower bound is commonly used to generate a so-called uncertainty ellipse which characterizes the spatial variance distribution of an efficient estimate, i.e. an estimate which achieves the lower bound. The aim of this work is to identify those relative sensor-target geometries which result in a measure of the uncertainty ellipse being minimized. Deeming such sensor-target geometries to be optimal with respect to the chosen measure, the optimal sensor-target geometr...

• Type: Journal
• Year: 2010
• Where: AUTOMATICA
• Authors: Adrian N. Bishop, Baris Fidan, Brian D. O. Anderson, Kutluyil Dogancay, Pubudu N. Pathirana
• Keywords: AUTOMATICA 2010, Lower Bound, Relative Sensor-target, Uncertainty Ellipse