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Intelligent RF-IR Data Fusion using Artificial Intelligence Techniques

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-14-C-7713
Agency Tracking Number: B2-1949
Amount: $881,991.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: MDA12-T002
Solicitation Number: 2012.A
Solicitation Year: 2012
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-02-18
Award End Date (Contract End Date): 2016-02-17
Small Business Information
CA Suite 310
San Mateo, CA 94402-2513
United States
DUNS: 608176715
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Richard Stottler
 Principal Investigator
 (650) 931-2700
Business Contact
 Carolyn Maxwell
Title: Contracts Manager
Phone: (650) 931-2700
Research Institution
 U. of Dayton Research Institute
 Linda Young
300 College Park
Dayton, OH 45469-0104
United States

 (937) 229-2919
 Nonprofit College or University

The ultimate goal of this proposed effort is to better utilize disparate sensor data to perform better correlations and lethality assessments through the development of a sensor data fusion system using artificial intelligence (AI) techniques and the concept of extracting features from raw sensor data and reasoning about those features and their hypothetical association with hypothesized objects. Building on the Phase I success, we will develop low-level sensor signal processing code and high-level feature extraction software to determine physical and kinematic features of the sensed objects. We will begin with the specifications for the primary sensors and descriptions of the primary targets of interest as well as the operational situations that are expected. An important objective of this Phase II effort is to gather additional experiment data to determine operational accuracy and limitations, validate our approach, and supplement existing databases. Using the existing and collected data as a basis, we can perform knowledge elicitation with our UDRI sensor experts to determine what features can be reliably extracted from the sensor data and in what situations. Based on what features are extractable, we can develop the automated reasoning to take advantage of them for both correlation and classification.

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

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