Defeating Emplaced Improvised Explosive Devices (IED) Using Fusion Algorithms

IEDs are primary insurgent weapons that have caused almost half the U.S. casualties in Iraq. Our response has included persistent surveillance and electronic warfare to defeat Radio-Controlled IEDs. The CREW Duke system developed by SRC has been a phenomenal success in jamming RC-IEDs and has saved countless lives. RF signal characteristics and vehicle positions are recorded for post-mission analysis in logs that provide a sampling of the RF environment and history of U.S. convoy movements. Long-term GMTI radar detections of moving targets are also being recorded to support manual forensic analysis. In Phase II, TSC and SRC propose to develop and demonstrate algorithms to fuse data from GMTI radar and CREW Duke logs to predict IED attacks so that convoys can be warned to avoid high risk areas or change their operating procedures. In Phase I, TSC tested our existing anomalous movement detection algorithms using measured radar data, and SRC applied a Depedency Analysis technique to collected CREW Duke logs. In Phase II, our team will identify the best combination of information on target movement, RF environment and geospatial factors to predict IED attacks.  We will develop and demonstrate a fusion algorithm to reliably predict attacks, and recommend radar and CREW improvements to enhance performance.   BENEFIT: This technology can alert U.S. military convoys to potential IED attacks in time to choose alternate routes or change operating procedures. Our team plans to build a fully automated system that retrieves CREW Duke mission logs and sends these to a central processor for fusion with GMTI radar data and intelligence reports, and then provide alerts to ground commanders. The technology can also be applied to combine radar, electronic intercept, and other sensor data to enhance border security and improve criminal surveillance.