Algorithms for Threat Detection in Sensor Systems for Analyzing Chemical and Biological Systems Based on Compressive Sensing and L1 Related Optimization

研究人员打算在检测化学和生物材料的传感器系统中产生新的有效的数学算法和方法。接下来，他们打算将这项技术直接转让给那些致力于减少生物和化学攻击对家园威胁的人。他们将使用的新技术主要来自信息科学、图像科学和物理学，涉及谐波分析、机器学习、优化和偏微分方程。特别是，他们打算为使用激光雷达主动传感的多组分气溶胶分解和被动传感中的蒸汽混合物提供有用的算法。他们将使用最近发展起来的思想和算法，广泛地说，从压缩感知和L1相关优化应用于高光谱成像（最近被海豹突击队在本拉登的行动中使用）、解混、模板匹配、异常检测、聚类、变化检测和端元计算。他们将利用他们的优化技术改进相关的经典学习技术，如支持向量机。他们还将使用具有先验信息的非局部方法的机器学习的思想，以便在从各种传感器收集的数据中分割和识别对象。最后，他们将考虑物理因素，如羽流耗散，作为进行空间分割和识别所需的先验信息的一部分。十多年来，美国政府一直在开发基于激光的传感器，用于在安全距离内对大气中的气溶胶进行定位和分类。有必要将生物来源的气溶胶与烟雾和灰尘等无关物质区分开来。通常，气溶胶的混合物是存在的，决定是否存在威胁是很重要的。本项目旨在将包含这种混合物的数据分解为其单独的组件。调查人员在这里已经取得了一些成果。这是这项工作所关注的一个例子。化学和/或生物污染可能发生在地面或空气中。问题是确定化学和生物威胁的存在和浓度，并跟踪云的动态。这里所做的研究是相关的所有传感器模式中使用的这种类型的威胁检测。其中包括最先进的激光雷达传感器、红外辐射测量仪和高光谱传感器。在有潜在威胁的情况下，追踪大气中的羽流尤为重要。鉴于化学和生物大规模杀伤性武器的威胁，这里提出的工作类型对我们国家的安全至关重要。

The investigators intend to generate new and effective mathematical algorithms and methodologies in sensor systems for the detection of chemical and biological materials. Next, they intend to transfer this technology directly to those working towards reducing the threat to the homeland of biological and chemical attack. The new techniques they will use come primarily from information science, image science and physics, involving harmonic analysis, machine learning, optimization and partial differential equations. In particular they intend to provide useful algorithms for multi-component aerosol unmixing for active sensing using LiDAR and for mixtures of vapors in passive sensing. They will use ideas and algorithms recently developed, broadly speaking, from compressive sensing and L1 related optimization which were applied to hyperspectral imaging (recently used by Navy SEALS in the Bin Laden take down), unmixing, template matching, anomaly detection, clustering, change detection and endmember computation. They will improve relevant classical learning techniques, such as support vector machine, using their optimization techniques. They will also use ideas from machine learning with nonlocal means with prior information, in order to segment and identify objects in data collected from all sorts of sensors. Finally, they will factor in physics, such as plume dissipation, as part of the prior information needed to do spatial segmentation and identification.The US government has been developing laser-based sensors for locating and classifying aerosols in the atmosphere at safe standoff ranges for more than a decade. There is a need to distinguish aerosols of biological origin from indifferent materials such as smoke and dust. Often, mixtures of aerosols are present and it is important to decide whether a threat exists. This project is intended to resolve data containing such a mixture into their separate components. Some success has already been obtained here by the investigators. This is an example of what this work concerns. A chemical and/or biological contamination might occur on the ground or in the air. The problem is to determine the presence of and concentration of chemical and biological threats and to track the dynamics of the cloud. The research done here is relevant to all the sensor modalities used in this type of threat detection. These include state-of-the-art LiDAR sensors, infrared radiometry and hyperpectral spensors. Plume tracking through the atmosphere is particularly important in a potential threat situation. The type of work proposed here is basic to our nation's security, given the threat posed by chemical and biological WMD's.