SBIR Phase I: Isolating, Locating and Tracking Target Anomalies in Ultra-Wideband (UWB) Sensor Data

This Small Business Innovation Research Phase I project will capitalize on evolving concepts for sensor optimization that will facilitate homeland security by detecting and tracking human targets through foliage, building walls, fog, and other opaque materials. This work will explore repeatable methods to extract target anomalies from ground sensor data and employ statistical methods to mitigate false alarms and track these anomalies. This work capitalizes on target-induced amplitude and phase distortions in the reflected signal to generate an observation set comprised of isolated and potentially classifiable anomalies. The combined approach facilitates extraction of challenging targets such as dismounted terrorist forces in the presence of clutter and neighboring targets. Statistical methods to track these targets also facilitate data fusion from numerous sensors, which simultaneously increases target discrimination and sensor coverage. This technology will have broad application in airport security, hostile police action, high school and other hostage events, and for search and rescue.