CAT Learning Algorithm Workbench (CLAW)

Current countermeasure anti-torpedo (CAT) systems use explicit logic to direct intercepts resulting in an inability to adapt to the complexities of the stochastic marine environment. The CAT Learning Algorithm Workbench (CLAW) is an analytical research testbed capable of comparing the effectiveness of different machine learning approaches to optimize and automate anti-torpedo fire control and develop criteria concepts for discriminating among them. By applying recent developments in intelligent algorithms to existing simulations and models in the program of record using an instrumented test environment, investigators can identify the most promising designs for using adaptive learning in the Torpedo Warning System. The benefit of the approach is to harden battle group defenses against torpedo salvos by finding optimal fire control solutions and automating the launch decision process.