Motion camouflage in a stochastic setting

Recent work has formulated 2- and 3-dimensional models and steering control laws for motion camouflage, a stealthy pursuit strategy observed in nature. Here we extend the model to encompass the use of a high-gain pursuit law in the presence of sensor noise as well as in the case when the evader's steering is driven by a stochastic process, demonstrating (in the planar setting) that motion camouflage is still accessible (in the mean) in finite time. We also discuss a family of admissible stochastic evader controls, laying out the groundwork for a future game-theoretic study of optimal evasion strategies.