Particle Guidance: Applying POMDPs to the Optimization of Mid-Course Guidance Laws for Long-Range Missiles

摘要

During the mid-course phase of an air-to-air missile, choosing the optimal Guidance Point (GP) so as to maximize lock-on success and minimize intercept time is critical. Given low computational resources available on board and a very constrained maneuvering time frame, GP-based algorithms must be efficient. We suggest an innovative approach using Reinforcement Learning (RL) to produce finite state controllers that can be executed efficiently - using table lookup - to meet the strict time limits of a target engagement. Instead of hand-crafting a GP-picking algorithm for every combination of sensor and aircraft configuration, one promising alternative models a missile-target engagement as a Partially Observable Markov Decision Process (POMDP) and automatically generates a controller for picking the best GP by solving the POMDP model. Using a recently developed offline algorithm called Monte Carlo Value Iteration (MCVI) we constructed continuous-state POMDP models and solved them directly, without discretizing the entire state space.