Anticipating stochastic observation loss during optimal target tracking by a small Aerial Vehicle

Abstract

Motivated by tracking problems involving a fixed-speed, fixed-altitude Unmanned Aerial Vehicle (UAV) that should maintain a nominal distance from ground target, an optimal feedback control is rigorously developed to anticipate both unknown future target trajectories and the possibility for the loss of observations due to sensory interference. Stochasticity is introduced the problem by assuming that the target motion can be modeled as a random walk, and by assuming that the observation times of the target are exponentially distributed. A Bellman equation based on an approximating Markov chain that is consistent with the stochastic kinematics is used to compute a control policy that minimizes the expected value of a cost function based on a nominal UAV-target distance. Numerical simulations illustrate the benefit to anticipating for stochastic observation loss.