Knowledge-Based Airborne Hybrid System for Sparse Minefield Detection

Award Information
Agency:
Department of Defense
Amount:
$69,992.00
Program:
SBIR
Contract:
W15P7T-10-C-S408
Solitcitation Year:
2009
Solicitation Number:
2009.2
Branch:
Army
Award Year:
2010
Phase:
Phase I
Agency Tracking Number:
A092-078-1391
Solicitation Topic Code:
A09-078
Small Business Information
Migma Systems, Inc.
1600 Providence Highway, Walpole, MA, 02081
Hubzone Owned:
N
Woman Owned:
Y
Socially and Economically Disadvantaged:
Y
Duns:
125933916
Principal Investigator
 Bo Ling
 President & CEo
 (508) 660-0328
 bling@migmasys.com
Business Contact
 Bo Ling
Title: President & CEO
Phone: (508) 660-0328
Email: dsmetana@irvine-sensors.com
Research Institution
N/A
Abstract
Landmines come in a variety of shapes and sizes. They can be square, round, cylindrical, or bar-shaped. The casing can be metal, plastic, or wood. Traditional techniques to detect landmines are both dangerous and time consuming. Airborne-based landmine detection system often uses high resolution optical sensors for the target detection. They use various image processing techniques that fall in two broad categories – signature-based and anomaly detections. Since landmines appeared in airborne IR images often don’t show stable signatures, signature-based approach is less robust. Anomaly detection usually generates many false alarms, making the system practically less useful. To overcome these challenges, in Phase I, we propose to develop a suite of anomaly detectors and false alarm mitigators for the target detections. We combine these detections in a minefield composite image to form a sparse minefield. Degree of randomness algorithms will be developed to detect the minefield. To further improve the system performance, we propose to design a knowledge base using mixture ontology. This knowledge base is capable of reasoning and can automatically select parameters and their settings based on the operating conditions and target types. External data sources can also be incorporated into this knowledge base.

* information listed above is at the time of submission.

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