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: 0914567
• 负责人: Selim Esedoglu
• 金额: $ 23.43万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0914567&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 harmful contamination 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 number of stationary or moving sensors receive and record infrared radiation from the scene containing the cloud (plume) in addition to the radiation from other elements in the scene, such as the background and intervening atmosphere. The sensors are assumed to be able to resolve the spectrum of the receivedtotal radiation and the spectral signatures of chemicals of interest 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，该建议引入了新的策略，大大降低了反演的复杂性。PI及其合作者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，将成为应对这些挑战的核心。这项研究对反恐工作有直接和直接的影响，例如在未知的战场以及城市地区监视和发现有害污染源。一种期望的能力是根据来自传感器阵列的被动远程测量来重建和预测侵入性化学和/或生物云的位置和范围。只有数量非常有限的固定或移动传感器接收和记录来自包含云（羽流）的场景的红外辐射，以及来自场景中其他元素（如背景和干预大气）的辐射。假定传感器能够分辨接收到的总辐射的光谱，并且可能已知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，检测源的位置和内容，预测羽流的行为，并最终最大限度地减少此类事件造成的损害。