Information space receding horizon control for multi-agent systems

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 multi-sensor optimal scheduling problem can be posed as a multi-agent 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 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 obviates the need to solve the computationally intractable multi-agent I-space DP problem and hence, makes the technique computationally tractable for such problems. The technique is tested on a simple numerical example which is nonetheless computationally intractable for existing solution techniques.