Center for Embedded Network Sensing

We propose a novel entropy-based sensor selection heuristic for localization. Given 1) a prior probability distribution of the target location, and 2) the locations and the sensing characteristics of a set of additional sensors, we would like to select an optimal additional sensor such that fusion of its measurements with existing information would yield the greatest entropy reduction of the target location distribution. The heuristic can select a sub-optimal additional sensor without retrieving the measurements of candidate sensors. The heuristic is computationally much simpler than the mutual information based sensor selection approaches for localization and tracking [1, 2]. Just as those existing approaches do, the heuristic greedily selects one sensor in each step.