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Automated pattern recognition methods to identify nuclear explosions

Award Information
Agency: Department of Defense
Branch: Defense Threat Reduction Agency
Contract: HDTRA119P0025
Agency Tracking Number: T182-005-0024
Amount: $149,730.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: DTRA182-005
Solicitation Number: 18.2
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-03-19
Award End Date (Contract End Date): 2019-10-18
Small Business Information
2928 South Buchanan Street Suite C-1
Arlington, VA 22206
United States
DUNS: 079156854
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Hyrum Laney
 Co-Founder / Executive Vice President
 (703) 371-0718
 hlaney@acornsi.com
Business Contact
 Hyrum W. Laney
Phone: (703) 371-0718
Email: hlaney@acornsi.com
Research Institution
N/A
Abstract

Reliable automated pattern recognition in the current system is limited by excessive noise and clutter combined with insufficient and/or ambiguous features. Our team consisting of AcornSI and Leidos thus propose a multi-step approach based on the combined effects of improved detection, feature extraction, phase identification, and global association. A key innovation here is creating a new classification feature and confidence based on machine vision to directly classify waveform spectrograms. Improved legacy and new features are then sent to a (possibly nested) support vector machine (SVM) to automatically determine event classifications with associated confidence scores.

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

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