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NAVY TECHNOLOGY ACCELERATION - Unmanned Surface Vehicle (USV) and Unmanned Underwater Vehicle (UUV) Autonomous Behavior Development

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-F-0114
Agency Tracking Number: N193-A02-0439
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N193-A02
Solicitation Number: 19.3
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-11-21
Award End Date (Contract End Date): 2020-04-20
Small Business Information
17150 W 95th Place
Arvada, CO 80007
United States
DUNS: 130770055
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Christopher Bowman
 President
 (303) 469-9828
 cbowman@df-nn.com
Business Contact
 Dr. Christopher Bowman
Phone: (303) 469-9828
Email: cbowmanphd@gmail.com
Research Institution
N/A
Abstract

At sea commanders must maintain situational awareness that includes a wide range of surface and subsurface contacts with multiple acoustic (AC), radio frequency (RF), optical (VIS), and thermal (IR) signatures. Automatic detection and threat classification can dramatically improve their response time and course of action decisions. This proposal demonstrates the feasibility of artificial intelligence machine learning neural nets by delivering a USV/UUV Naval Abnormal Signal Detection & Classification (NASDC) intelligent system prototype to learn operational normal background signatures and historical signatures for vessels of interest, plus other data from a varying subset of hybrid on-board sensors (e.g., differing combinations of AC, RF, VIS, and IR bands). The NASDC will be tested on historical and simulated data for in-stride detection of unknown abnormal temporal signatures and multi-spectral historical signature classification confidence scores. NASDC will also provide a categorization results trust score for each time window based upon the similarity of the test data to the full training set. NASDC will be trained and tested based upon noise models and acoustic performance simulations to characterize environments plus historical experiment data at Applied Ocean Sciences (AOS).

* Information listed above is at the time of submission. *

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