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

这个小型企业创新研究第一阶段项目将利用不断发展的传感器优化概念，通过树叶、建筑墙、雾和其他不透明材料来检测和跟踪人类目标，从而促进国土安全。这项工作将探索可重复的方法，从地面传感器数据中提取目标异常，并使用统计方法来减少虚警和跟踪这些异常。这项工作利用目标在反射信号中引起的幅度和相位失真来生成由孤立的和潜在地可分类的异常组成的观测集。这种结合的方法有助于在杂乱和邻近目标存在的情况下提取具有挑战性的目标，如下马的恐怖力量。跟踪这些目标的统计方法也有利于来自多个传感器的数据融合，这同时增加了目标识别和传感器覆盖。这项技术将在机场安检、敌对警察行动、高中和其他人质事件以及搜救中得到广泛应用。

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.