Information space receding horizon control

In this paper, we present a receding horizon solution to the problem of optimal sensor scheduling problem. The optimal sensor scheduling problem can be posed as a Partially Observed Markov Decision Process (POMDP) whose solution is given by an Information Space (I-space) Dynamic Programming (DP) problem. We present a simulation based stochastic optimization technique that, combined with a receding horizon approach, obviates the need to solve the computationally intractable I-space DP problem. The technique is tested on a simple sensor scheduling problem where a sensor has to choose among the measurements of N dynamical systems such that the information regarding the aggregate system is maximized over an infinite horizon.