CAT Learning Algorithm Workbench (CLAW)

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$149,979.00
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
N00024-12-P-4032
Award Id:
n/a
Agency Tracking Number:
N113-176-0016
Solicitation Year:
2011
Solicitation Topic Code:
N113-176
Solicitation Number:
2011.3
Small Business Information
625 Mount Auburn Street, Cambridge, MA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
115243701
Principal Investigator:
Wayne Thornton
Senior Scientist
(617) 491-3474
wthornton@cra.com
Business Contact:
Mark Felix
Contracts Manager
(617) 491-3474
mfelix@cra.com
Research Institution:
Stub




Abstract
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.

* information listed above is at the time of submission.

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