Optimizing Flight Paths Through Anti-Aircraft Gun Fire with Machine Learning
Abstract
We present a methodology intended to optimize the flight path through a flight corridor occupied by enemy anti-aircraft guns. This is relevant for all kinds of aircraft, missiles, and drones moving through air space that is fully or partially controlled by such guns. To this end we use Q-learning - a type of reinforcement (machine) learning - which tries to find the optimal strategy to avoiding the anti-aircraft guns through repeated semi-random flight path trials. Q-learning can produce an optimal flight path through the enemy fire without modeling the anti-aircraft guns directly. An adversary response is still needed, but this can come from a black box simulation, user input, real data, or any other source. Here, we use an in-house tool for generating the anti-aircraft fire. This tool simulates a close-in weapons system (CIWS) guided by a fire control radar and Kalman flight path prediction filters. Q-learning can also be supplemented with neural networks - so-called deep Q-learning (DQN) - to handle even more complex problems. In this work, we present results for a subsonic flight corridor pass of one anti-aircraft gun position using classic Q-learning (no neural networks).
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 17, 2021
- Accession Number
- AD1150252
Entities
People
- Bernt Almklov
- Esben Lund
- Runhild A. Klausen
Organizations
- NATO Science and Technology Organization