Information geometry of target tracking sensor networks

Abstract

In this paper, the connections between information geometry and performance of sensor networks for target tracking are explored to pursue a better understanding of placement, planning and scheduling issues. Firstly, the integrated Fisher information distance (IFID) between the states of two targets is analyzed by solving the geodesic equations and is adopted as a measure of target resolvability by the sensor. The differences between the IFID and the well known Kullback-Leibler divergence (KLD) are highlighted. We also explain how the energy functional, which is the "integrated, differential" KLD, relates to the other distance measures. Secondly, the structures of statistical manifolds are el..