Information Space Receding Horizon Control for Multisensor Tasking Problems.

Abstract

In this paper, we present a receding horizon solution to the problem of optimal scheduling for multiple sensors monitoring a group of dynamical targets. The term target is used here in the classic sense of being the object that is being sensed or observed by the sensors. This problem is motivated by the space situational awareness (SSA) problem. The multisensor optimal scheduling problem can be posed as a multiagent Markov decision process on the information space which has a dynamic programming (DP) solution. We present a simulation-based stochastic optimization technique that exploits the structure inherent in the problem to obtain variance reduction along with a distributed solution. This stochastic optimization technique is combined with a receding horizon approach which uses online solution of the control problems to obviate the need to solve the computationally intractable multiagent information space DP problem and hence, makes the technique computationally tractable. The technique is tested on a moderate scale SSA example which is nonetheless computationally intractable for existing solution techniques.