ATD: Collaborative Research: Multiscale and Stochastic Methods for Inverse Source Problems and Signal Analysis

ATD：协作研究：逆源问题和信号分析的多尺度随机方法

• 批准号: 1042998
• 负责人: Haomin Zhou
• 金额: $ 24.19万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1042998&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; Multiscale; Stochastic

The main goal of the proposed work is to develop new models, computational methodologies and related mathematical theory for remote sensing with applications in chemical and biological threat detections. In those applications, data is usually gathered by optical sensors and then processed to reconstruct or analyze the properties of sources, such as chemical or biological plume. The tasks are difficult due to a number of challenges. For examples, the problems are ill-posed, the source functions are often random in nature and the data is noisy and incomplete. The PIs and collaborators proposed to investigate three different, but closely related, aspects of some newly emergent remote sensing techniques. In data acquisition, they work on inverse random source problems for the Helmholtz equation. Such problems exist in a wide range of applications in optical science, remote sensing and medical imaging. They aim to develop novel and efficient strategies to reconstruct the distributions of random source functions from incomplete boundary data measurements and perform uncertainty assessments. In data processing, they develop wavelet based multiscale methods in conjunction with the PDE based non-local mean methods for image denoising and information extraction of 3-D Lidar images. The methods integrate several high level mathematical tools, such as geometrical partial differential equations (PDE's), multiscale wavelet transforms and calculus of variation, together with some special properties of the Lidar imagery to achieve better results with fast computations. In data analysis stage, the PIs and collaborators study a novel nonlinear de-mixing method based on Hilbert transform and empirical mode decomposition (EMD) for signal analysis. EMD are designed to handle nonlinear and non-stationary signals, which cannot be easily processed by the traditional wavelet or Fourier based methods. By using EMD, they can extract useful but hidden information through techniques such as instantaneous frequency analysis.Remote sensing techniques have gained unprecedent attentions due to new challenges in many disciplines including homeland security, military, geosciences, medical science and engineering. Specially, they have become one of the primary tools for data collections in unreachable, unfriendly or hazardous environments. For instance, a most recent advance in chemical or biological threat detection technology uses laser beams and optical sensors to collect signals from targets, such as aerosol plumes. Then the gathered data is processed to identify harmful agents. A key step to succeed is to determine the material properties of the sources, such as whether there exist certain chemical or biological agents, from the collected data sets. This requires solving the so called inverse problems. In practice, they are challenging due to a number of issues. The collected data is often incomplete, random and noisy, the aerosol plume is too thick to ``see'' signals from the center parts , the signatures of the harmful agents and normal aerosol particles are mixed and hard to be separated. In this proposal, the PIs focus their studies in three aspects of the most recent advances in remote sensing techniques with applications in chemical and biological threat detections. They aim to develop novel, robust and efficient computational methods and related mathematical theory to solve the inverse random source problems from incomplete data sets, to remove noise from the signals, and to separate the signatures of different aerosol particles so that harmful agents can be easily identified from the signals. In addition, another major objective is to integrate the research activities with education and training of undergraduate, graduate students and postdocs through seminars and courses.

拟议工作的主要目标是开发新的遥感模型、计算方法和相关数学理论，并应用于化学和生物威胁探测。在这些应用中，通常通过光学传感器收集数据，然后进行处理以重建或分析源的特性，例如化学或生物羽流。由于存在一些挑战，任务十分艰巨。例如，问题是不适定的，源函数通常是随机的，数据是有噪声的和不完整的。PI和合作者建议调查一些新兴遥感技术的三个不同但密切相关的方面。在数据采集方面，他们研究亥姆霍兹方程的逆随机源问题。这类问题在光学、遥感和医学成像等领域有着广泛的应用。他们的目标是开发新的和有效的策略，从不完整的边界数据测量重建随机源函数的分布，并进行不确定性评估。在数据处理方面，他们开发了基于小波的多尺度方法，结合基于PDE的非局部均值方法，用于图像去噪和三维激光雷达图像的信息提取。该方法集成了几个高层次的数学工具，如几何偏微分方程（PDE），多尺度小波变换和变分法，以及一些特殊性质的激光雷达图像，以实现更好的结果与快速计算。在数据分析阶段，PI和合作者研究了一种基于Hilbert变换和经验模式分解（EMD）的新型非线性去混合信号分析方法。经验模态分解（EMD）是为处理非线性、非平稳信号而设计的，传统的基于小波或傅立叶变换的方法很难处理这些信号。由于遥感技术在国土安全、军事、地球科学、医学和工程等诸多学科领域都面临着新的挑战，因此遥感技术得到了前所未有的重视。特别是，它们已经成为在无法到达，不友好或危险环境中进行数据收集的主要工具之一。例如，化学或生物威胁探测技术的最新进展使用激光束和光学传感器收集来自目标的信号，如气溶胶羽流。然后，对收集的数据进行处理，以识别有害物质。 成功的一个关键步骤是从收集的数据集中确定源的材料属性，例如是否存在某些化学或生物制剂。这需要解决所谓的逆问题。在实践中，由于一些问题，它们具有挑战性。由于气溶胶羽流太厚，无法“看到”来自中心部分的信号，有害物和正常气溶胶粒子的特征信号混合，难以分离，因此，气溶胶羽流的采集往往是不完整的、随机的和有噪声的。在这一建议中，研究所将重点放在遥感技术最新进展的三个方面，并将其应用于化学和生物威胁探测。 他们的目标是开发新的，强大的和有效的计算方法和相关的数学理论来解决不完整的数据集的逆随机源问题，从信号中去除噪声，并分离不同气溶胶颗粒的签名，以便可以很容易地从信号中识别有害物质。此外，另一个主要目标是通过研讨会和课程将研究活动与本科生、研究生和博士后的教育和培训相结合。