Collaborative Research: ATD (Algorithms for Threat Detection): Inverse Problems Methods in Chemical Threat Detection

合作研究：ATD（威胁检测算法）：化学威胁检测中的反问题方法

• 批准号: 0914561
• 负责人: Stanley Osher
• 金额: $ 29.34万
• 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: 2009-08-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0914561&HistoricalAwards=false
• 关键词: Collaborative; Research; ATD; Algorithms; Threat

This proposal introduces a class of novel inverse problems with applications to, and motivated by anti-terrorism efforts, such as surveillance and discovery of harmful comtamination sources in unknown battle fields as well as urban regions. Unlike the typical settings of a large class of inverse problems, the research involves inverting Radon transforms from very sparse samples and constraints involving parttial differential equations. These considerations present interesting challenges in both mathematical analysis and modeling as well as in the design and implementation of appropriate computational methods. In addition, this proposal introduces novel strategies which greatly reduce the complexity for the inversion.State-of-the-art numerical techniques that have been in development by the PI and his collaborators, such as the use of Bregman iteration in imaging and compressed sensing and inverse problem applications will be central in meeting these challenges. This research has immediate and direct implications for anti-terrorism efforts, such as surveillance and discovery of harmfulcontamination sources in unknown battlefields as well as urban regions.A desired capability is to reconstruct and predict the whereabouts and the extent of an offending chemical and/or biological cloud from passive, remote measurements from an array of sensors. A very limited numberof stationary or moving sensors receive and record infrared radiationfrom the scene containing the cloud (plume) in addition to the radiationfrom other elements in the scene, such as the background and interveningatmosphere. The sensors are assumed to be able to resolve the spectrum of the receivedtotal radiation and the spectral signatures of chemicals ofinterest may be known. This research will help to move the sensors to optimal locations, to detect the locations and contents of thesources, to predict the plumes' behavior and ultimately to minimize the damage caused by such events.

该建议介绍了一类新的反问题的应用程序，并出于反恐工作，如在未知的战场以及城市地区的有害污染源的监测和发现。与一大类反问题的典型设置不同，该研究涉及从非常稀疏的样本和涉及偏微分方程的约束中反演Radon变换。这些考虑在数学分析和建模以及适当的计算方法的设计和实现中提出了有趣的挑战。此外，该提案还引入了新的策略，大大降低了反演的复杂性。PI及其合作者一直在开发的最先进的数值技术，例如在成像和压缩传感以及逆问题应用中使用Bregman迭代，将是应对这些挑战的核心。 该研究对反恐工作有着直接和直接的意义，例如在未知战场和城市地区监视和发现有害污染源。一种期望的能力是通过传感器阵列的被动远程测量来重建和预测有害化学和/或生物云的位置和范围。一个非常有限数量的固定或移动的传感器接收和记录红外辐射的场景包含云（羽流）除了辐射从其他元素在场景中，如背景和干预的atmosphere。假定传感器能够分辨接收到的总辐射的光谱，并且可以知道感兴趣的化学品的光谱特征。这项研究将有助于将传感器移动到最佳位置，以检测烟雾的位置和内容，预测羽流的行为，并最终将此类事件造成的损害降到最低。