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Enhanced Event Detection with Seismic Listening Sensors

Award Information
Agency: Department of Defense
Branch: Army
Contract: W912HZ-09-C-0110
Agency Tracking Number: A082-114-1098
Amount: $729,968.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: A08-114
Solicitation Number: 2008.2
Timeline
Solicitation Year: 2008
Award Year: 2009
Award Start Date (Proposal Award Date): 2009-09-11
Award End Date (Contract End Date): 2011-09-11
Small Business Information
20 New England Business Center
Andover, MA 01810
United States
DUNS: 073800062
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Aram Faghfouri
 Principal Scientist
 (978) 689-0003
 faghfouri@psicorp.com
Business Contact
 B. David Green
Title: President and CEO
Phone: (978) 689-0003
Email: green@psicorp.com
Research Institution
N/A
Abstract

Physical Sciences Inc. (PSI) proposes a Phase II program to develop, test and implement in a hardware/software system an advanced set of statistics-based algorithms for detecting anomalous events for a wide range of seismic activity levels. Phase I quantitatively demonstrated the feasibility of our statistics-based approach, yielding event detection with p_d > 90%, P_fa < 1%, location accuracy < 15m, and data compression rate > 20. Phase II will develop algorithms that can analyze a long period of signal from an environment, measure its activity level using entropy and multifractal dimensions, analyze its trends, learn the relationships between states of the signal through statistical learning methods, and then detect the time and location of unusual events using innovative statistical decision making techniques. These algorithms will be capable of detecting low level (e.g., a rural area) and high level (e.g., marketplace) seismic activities in the background and adjust their thresholds, metrics, and decision making methods for increasing their probability of detection to above 95%, reducing their false alarm to less than 1%, and location detection error to less than 30m. In addition, these algorithms will reduce the required storage space for the signals by at least 2 orders of magnitude.

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

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