Algorithms for Threat Detection (ATD): adaptive sensing and sensor fusion for real time chemical and biological threats

威胁检测 (ATD) 算法：针对实时化学和生物威胁的自适应传感和传感器融合

• 批准号: 0914856
• 负责人: Andrea Bertozzi
• 金额: $ 45万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0914856&HistoricalAwards=false
• 关键词: Algorithms; Threat; Detection; ATD; adaptive

The investigator plans a three year research program to develop algorithms for sensor systems for the detection of chemical and biological materials. This work builds on prior research of the investigator and her colleagues involving autonomous mobile sensors for environmental sampling and algorithms for understanding hyperspectral imagery data. The research program involves the design of multiscale, multimodal sensing and detection algorithms, using data from both standoff detection and point detection from sensors mounted on mobile autonomous platforms. This data-intensive research depends on the modes of data available and their spatio-temporal resolution, viewpoints, and spectral resolution. The work includes the design and construction of a numerical simulator for the project, that incorporates various sensing modalities and on which algorithms are tested against against field data supplied by the government. In addition, mobile sensing algorithms are validated and tested at a laboratory multi-vehicle wireless testbed involving simpler sensors as a proxy for field sensor data. The research exploits recent algorithmic advances in image analysis and reconstruction from high dimensional data. These include, but are not limited to, compressive sensing methods, total variation minimization methods, hybrid wavelet-PDE algorithms for data fusion at different scales, hybrid geometric-stochastic algorithms for real time path planning and analysis, and nonlinear filtering.The ability to detect and analyze biological and chemical threats in real time is essential to the future security of our country. Recent advances in sensor design now allow for rapid collection of information from multiple vantage points, involving multispectral sensing modalities. Where we are lacking is the ability to rapidly process and understand evolving information from diverse platforms to accurately identify and track the threat. This challenging problem requires new ideas for mathematical algorithm design to fuse the diverse data and provide accurate detection with both a low false alarm rate and detection delay. This research program develops new methods for high performance data processing and new fast algorithms for identification, in order to optimally utilize state-of-the-art and future sensor technology.

研究人员计划进行一项为期三年的研究计划，以开发用于检测化学和生物材料的传感器系统的算法。 这项工作建立在研究人员及其同事先前的研究基础上，涉及用于环境采样的自主移动的传感器和用于理解高光谱图像数据的算法。 该研究计划涉及多尺度，多模态传感和检测算法的设计，使用来自安装在移动的自主平台上的传感器的远距离检测和点检测的数据。 这种数据密集型研究取决于可用数据的模式及其时空分辨率、视点和光谱分辨率。 这项工作包括为该项目设计和建造一个数值模拟器，该模拟器采用了各种传感模式，并根据政府提供的现场数据对算法进行了测试。 此外，移动的传感算法进行了验证和测试，在实验室多车辆无线试验台涉及简单的传感器作为现场传感器数据的代理。 这项研究利用了最近在图像分析和从高维数据重建算法的进展。 这些方法包括但不限于压缩感知方法、总变差最小化方法、用于不同尺度数据融合的混合小波-PDE算法、用于真实的时间路径规划和分析的混合几何-随机算法以及非线性滤波。真实的时间检测和分析生物和化学威胁的能力对我国未来的安全至关重要。 传感器设计的最新进展现在允许从多个Vantage位置快速收集信息，包括多光谱传感模式。 我们缺乏的是快速处理和理解来自不同平台的不断变化的信息的能力，以准确识别和跟踪威胁。 这一具有挑战性的问题需要数学算法设计的新思路，以融合不同的数据，并提供准确的检测与低误报率和检测延迟。 该研究计划开发了高性能数据处理的新方法和新的快速识别算法，以最佳地利用最先进的和未来的传感器技术。