  This paper presents a potential game-based method for non-myopic planning of
mobile sensor networks in the context of target tracking. The planning
objective is to select the sequence of sensing points over more than one future
time steps to maximize information about the target states. This multi-step
lookahead scheme is studied to overcome getting trapped at local information
maximum when there are gaps in sensing coverage due to constraints on the
sensor platform mobility or limitations in sensing capabilities. However, the
long-term planning becomes computationally intractable as the length of planing
horizon increases. This work develops a gametheoretic approach to address the
computational challenges. The main contributions of this paper are twofold: (a)
to formulate a non-myopic planning problem for tracking multiple targets into a
potential game, the size of which linearly increases as the number of planning
steps (b) to design a learning algorithm exploiting the joint strategy
fictitious play and dynamic programming, which overcomes the gaps in sensing
coverage. Numerical examples of multi-target tracking demonstrate that the
proposed method gives better estimation performance than myopic planning and is
computationally tractable.
