ATD: A Novel Statistical Framework for Sensor Fusion

该项目引入了一个新的框架，用于传感器融合算法的发展，该算法结合了因子分析统计文献中的思想。该项目强调在检测问题上的应用，旨在开发灵活的算法，这些算法对错误分布的高斯性和传递函数的线性等常见假设的违反具有鲁棒性。研究人员描述的框架包括作为特殊情况的一些最广泛使用的传感器融合工具，如线性和卡尔曼融合滤波器，它们被推广到包括非线性、非高斯误差、伴随变量和传感器之间的相关性的影响。该项目与更传统的因子分析文献（具有悠久的历史，特别是在社会科学领域）的一个区别在于，在传感器融合的背景下，潜在因素具有真正的物理意义，因此通常可以收集可用于学习模型结构特征的训练集。这些训练集的可用性使研究人员能够开发复杂的传感器融合模型，如果没有它们，这些模型的参数将无法识别。除了提供具有广泛适用性的传感器融合的一般框架外，该项目还探索了这些技术在高光谱图像分析相关问题中的应用，特别是在线性监督和无监督分解的背景下。最近的技术进步极大地增加了各种领域的数据来源和收集的数据量。在从国防和国家安全到环境科学和工业过程的应用中，有效利用这些大量信息是一个关键挑战。该项目开发了下一代传感器融合工具，即以最佳方式组合来自多个站点的传感器的信息，或通过以非常高的频率监测单个传感器。在这个项目中开发的算法将比大多数最先进的方法更健壮和普遍适用。此外，由于研究人员与国家实验室和其他政府机构的外部团体之间的合作，本提案中开发的工具将对国防部完成其任务的能力产生深远的影响。

This project introduces a novel framework for the development of sensor fusion algorithms that incorporates ideas from the statistical literature on factor analysis. The project emphasizes applications to detection problems, and aims to develop flexible algorithms that are robust to violations of common assumptions such as Gaussianity of error distributions and linearity of transfer functions. The framework described by the investigators includes as special cases some of the most widely used tools for sensor fusion, such as linear and Kalman fusion filters, which are generalized to include the effects of non-linearities, non-Gaussian errors, concomitant variables, and correlations across sensors. One aspect that distinguishes this project from the more traditional literature on factor analysis (which has a long history, particularly in the social sciences) is that, in the context of sensor fusion, the latent factors have real physical meaning and therefore it is often possible to collect training sets that can be used to learn structural features of the model. The availability of these training sets allow the researchers to develop complex models for sensor fusion whose parameters would not be identifiable without them. In addition to providing a general framework for sensor fusion with wide applicability, this project also explores the application of these techniques to problems related to hyperspectral image analysis, particularly in the context of linear supervised and unsupervised unmixing.Recent technological advances have dramatically increase both the sources of data and the amount of data being collected in all kinds of fields. Making efficient use of these large amounts of information is a critical challenge in applications ranging from defense and national security to environmental sciences and industrial processes. This project develops the next generations of tools for sensor fusion, i.e., to optimally combine information arising from sensors located in multiple sites, or by monitoring a single at a very high frequency. The algorithms developed in this project will be more robust and generally applicable than most state-of-the-art approaches. In addition, because of the collaborations between the investigator and external groups at national laboratories and other government agencies, the tools developed in this proposal will have a deep impact on the ability of the Department of Defense to accomplish its missions.