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

研究人员打算在传感器系统中产生新的有效的数学算法和方法，用于检测化学和生物材料。下一步，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国土威胁的人。他们将使用的新技术主要来自信息科学、图像科学和物理，涉及调和分析、机器学习、优化和偏微分方程。特别是，它们打算提供有用的算法，用于利用激光雷达进行主动传感的多组分气溶胶的混合，以及被动传感中的蒸汽混合物。他们将使用最近开发的思想和算法，广义上讲，这些思想和算法来自压缩传感和L1相关优化，这些优化应用于高光谱成像(最近由海豹突击队在本·拉登击落中使用)、分解、模板匹配、异常检测、聚类、变化检测和端元计算。它们将利用它们的优化技术来改进相关的经典学习技术，如支持向量机。他们还将使用机器学习中的想法，结合非本地方法和先验信息，以便在从各种传感器收集的数据中分割和识别对象。最后，他们将把烟羽消散等物理因素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，用于在安全的对峙范围内对大气中的气溶胶进行定位和分类。有必要区分生物来源的气溶胶和烟尘等无关紧要的物质。通常，存在各种气溶胶混合物，确定是否存在威胁是很重要的。该项目旨在将包含此类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功。这是这项工作所涉及的一个例子。化学和/或生物污染可能发生在地面或空气中。问题是要确定化学和生物威胁的存在和集中程度，并跟踪云的动态。这里所做的研究与这种类型的威胁检测中使用的所有传感器模式相关。这些包括最先进的激光雷达传感器、红外辐射测量和超谱散射器。在潜在威胁的情况下，羽流在大气层中的跟踪尤为重要。鉴于化学和生物大规模杀伤性武器构成的威胁，这里提出的工作类型对我国的安全是基本的。

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.