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

基于压缩感知和 L1 相关优化的用于分析化学和生物系统的传感器系统中的威胁检测算法

• 批准号: 1118971
• 负责人: Stanley Osher
• 金额: $ 119.87万
• 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: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1118971&HistoricalAwards=false
• 关键词: Algorithms; Threat; Detection; Sensor; Systems

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.

研究人员打算在传感器系统中生成新的有效的数学算法和方法，以检测化学和生物材料。接下来，他们打算将这项技术直接转移到旨在减少对生物和化学攻击国土威胁的人。他们将使用的新技术主要来自信息科学，图像科学和物理学，涉及谐波分析，机器学习，优化和部分微分方程。特别是，它们打算为多组分气溶胶固定算法提供有用的算法，用于使用LiDAR和被动传感中的蒸气混合物进行主动传感。他们将使用最近开发的思想和算法，从广义上讲，从压缩感应和与L1相关的优化中，这些优化应用于高光谱成像（最近由海军海豹突击队在Bin Laden取下中使用），不混合，模板匹配，异常检测，聚类，聚类，更改检测和Endmember计算。他们将使用其优化技术来改善相关的经典学习技术，例如支持向量机。他们还将使用具有先验信息的非本地手段的机器学习中的想法，以分割和识别从各种传感器收集的数据中的对象。最后，它们将考虑物理学，例如羽流耗散，这是进行空间分割和识别所需的先前信息的一部分。美国政府一直在开发基于激光的传感器，用于在安全的僵局中定位和分类大气中的气溶胶范围超过十年。有必要将生物起源的气溶胶与烟雾和灰尘等无动于衷的材料区分开。通常，存在气溶胶的混合物，重要的是决定是否存在威胁。 该项目旨在解决将包含这种混合物的数据解决到其单独的组件中。调查人员已经在这里获得了一些成功。这是这项工作所关注的一个例子。地面或空气中可能发生化学和/或生物污染。问题是确定化学和生物威胁的存在和浓度，并跟踪云的动力学。 这里进行的研究与此类威胁检测中使用的所有传感器方式有关。其中包括最先进的激光雷达传感器，红外辐射指定和光谱弹头。在潜在的威胁情况下，穿越大气的羽状追踪尤其重要。鉴于化学和生物WMD构成的威胁，这里提出的工作类型是我们国家安全的基础。