FTC of hidden Markov process with application to resource allocation in air operation

This paper investigates the feedback control of hidden Markov process (HMP) in the face of loss of some observation processes. The control action facilitates or impedes some particular transitions from an inferred current state in the attempt to maximize the probability that the HMP is driven to a desirable absorbing state. This control problem is motivated by the need for judicious resource allocation to win an air operation involving two opposing forces. The effectiveness of a receding horizon control scheme based on the inferred discrete state is examined. Tolerance to loss of sensors that help determine the state of the air operation is achieved through a decentralized scheme that estimates a continuous state from measurements of linear models with additive noise. The discrete state of the HMP is identified using three well-known detection schemes. The sub-optimal control policy based on the detected state is implemented on-line in a closed-loop, where the air operation is simulated as a stochastic process with SimEvents, and the measurement process is simulated for a range of single sensor loss rates.