Multi-Target High Probability of Kill Weapons Engagement - MP 129-11

The torpedo threat to U.S. and coalition naval forces is real and growing. Incorporating machine learning into the fire control piece for the Torpedo Warning System (TWS) can help to increase the probability of kill for the large number of possible torpedo threats. Metron, Inc. proposes a unique solution using a machine learning algorithm to provide better performance across a wider solution space than the current program-of-record approach. One of the primary machine learning approaches we are considering is the use of the approximate dynamic programming (DP) algorithm. The goal of our algorithm is to maximize the probability of kill in a setting with multiple concurrent hostile torpedoes. The stochastic disturbance in the algorithm will take the form of a Gaussian Process Model with squared exponential covariance, accounting for the dynamic and uncertain information surrounding TWS. For the Phase I Option, Metron will produce a design to integrate the machine learning algorithm into the Naval Simulation System (NSS) to demonstrate our technology. Finally, Metron will run several simulations and gather metrics such as probability of kill and probability of false alarm for Countermeasure Anti-Torpedoes (CATs).