Reinforcement learning for angle-only intercept guidance of maneuvering targets

Abstract

We present a novel guidance law that uses observations consisting solely of seeker line-of-sight angle measurements and their rate of change. The policy is optimized using reinforcement meta-learning and demonstrated in a simulated terminal phase of a mid-course exo-atmospheric interception. Importantly, the guidance law does not require range estimation, making it particularly suitable for passive seekers. The optimized policy maps stabilized seeker line-of-sight angles and their rate of change directly to commanded thrust for the missile's divert thrusters. Optimization with reinforcement meta-learning allows the optimized policy to adapt to target acceleration, and we demonstrate that the policy performs better than augmented zero-effort miss guidance with perfect target acceleration knowledge. The optimized policy is computationally efficient and requires minimal memory, and should be compatible with today's flight processors. (C) 2020 Elsevier Masson SAS. All rights reserved.