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.

该提案引入了一类新的逆问题，并由反恐努力进行应用，并引起了诸如未知战场和城市地区的有害稳定资源的诸如监视和发现有害的稳定源。与大类反问题的典型设置不同，该研究涉及反转ra倒的稀疏样本和涉及零件微分方程的约束。这些考虑因素在数学分析和建模以及适当的计算方法的设计和实施中都提出了有趣的挑战。此外，该提案还介绍了新的策略，这些策略大大降低了反转的复杂性。PI及其合作者正在开发的状态数值技术，例如将Bregman迭代在成像中使用和压缩感测和倒数问题的应用将是应对这些挑战的核心。 这项研究对反恐努力具有直接的直接影响，例如在未知战场和城市地区发现和发现有害污染源的监视和发现。所需的能力是重建和预测来自自由式的远程测量，来自传感器的有源化学和/或来自远程测量的有问题的化学和/或生物云的范围。除了从场景中的其他元素（例如背景和介入的趋势）中，除了构成云（羽流）的场景外，一个非常有限的数量固定或移动传感器从包含云（羽流）的场景接收和记录红外辐射。假定传感器能够解析接收到的辐射的光谱，并且可能已知具有引人入胜的化学物质特征。这项研究将有助于将传感器转移到最佳位置，检测这些象所的位置和内容，以预测羽毛的行为，并最终最大程度地减少这些事件造成的损害。