An Analysis of the Mutual Information Upper Bound for Sensor-Subset Selection

Abstract

The ability to rapidly select an optimal subset of sensors is of critical importance in massive multi-sensor target tracking. Various information metrics exist for selecting the subset of sensors that is most informative with respect to the target being tracked. Moreover, information bounds were proposed as approximate metrics in order to speed up the selection algorithms. In this paper, we provide an analysis on the information loss and its impact on the subset selection problem when employing an information upper bound instead of the exact mutual information metric. We design several greedy sensor-selection algorithms that sequentially evaluate the exact mutual information between a set of sensors and the target. Subsequently, we compare these algorithms with a sensor-selection method that employs an information upper bound and highlight situations where the latter finds sub-optimal solutions.