Cognitive Multi-Sensor Improvised Explosive Device (IED) Detection Technologies (COMIDT)

Up to 40% of all U.S. and coalition deaths in both Iraq and Afghanistan have been due to improvised explosive devices (IEDs). In addition, the Department of Homeland Security reports that more than 600 IED incidents occur worldwide each month outside of these regions, including within the U.S. While IED detection technologies continue to improve, insurgents and others who place IEDs are highly adaptive, using homemade explosives, non-metallic shrapnel and housings, and remote controlled detonators. As a result, current detectors, which generally focus on a specific aspect of the IED anatomy, are subject to very high rate of false positives. This greatly slows mission completion, wastes resources, and further endangers the warfighter. To resolve this problem, an extensible and distributed system is needed, capable of fusing multiple sensors based on geolocation to distinguish probable IEDs from false alarms. Ricciardi Technologies and the University of Washington will define this system, select explosive, radar, and radio frequency detection sensors, and define a common sensor interface, data normalization middleware, and reference databases. The team will also define the distributed architecture, cognitive engines to determine the probability of an IED, develop an integration plan, and build a simulation/proof of concept of the technology. BENEFIT: HSRC estimates $29.4 billion has been spent to date on counter IED technologies worldwide, and that $23.2 billion in additional products and services will be spent by 2012. $5.6 billion is currently focused on detection technologies. This addresses the growing IED threat, which Defense IQ estimates will rise 30% in the coming year. By leveraging an extensible and distributed interface for disparate sensors, each detecting different components of an IED, and interpreting the relative probability with each added signal, false positives will be significantly reduced, allowing more fluid movement of troops and resources to disable the real threats. The system will not only be able to utilize standard DoD explosive and radio frequency detectors as well as ground penetrating radar, but also third party sensors. In some cases, this may even include sensors the insurgents themselves have placed. The ability to use different and changing sensor networks will also make it more difficult for insurgents to adapt new strategies in response to these detectors. Once matured, this interface and architecture will also have applications in consumer, medical, safety, agricultural, and environmental sensor fusion areas; particularly the navigation and artificial vision fields of the $21 billion robotics market.