ATD: A Novel Statistical Framework for Sensor Fusion

ATD：传感器融合的新型统计框架

• 批准号: 1322216
• 负责人: Abel Rodriguez
• 金额: $ 57.52万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1322216&HistoricalAwards=false
• 关键词: ATD; Novel; Statistical; Framework; Sensor

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.

该项目介绍了一种新的框架，传感器融合算法的发展，结合了统计文献的因素分析的想法。该项目强调检测问题的应用，旨在开发灵活的算法，这些算法对违反常见假设（如误差分布的高斯性和传递函数的线性度）具有鲁棒性。研究人员所描述的框架包括一些最广泛使用的传感器融合工具的特殊情况，如线性和卡尔曼融合滤波器，这些滤波器被概括为包括非线性，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，潜在因子具有真实的物理意义，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用于传感器融合的复杂模型，如果没有它们，这些模型的参数将无法识别。除了为传感器融合提供一个具有广泛适用性的通用框架外，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解混的背景下。 有效利用这些大量的信息是从国防和国家安全到环境科学和工业过程的应用中的一个关键挑战。 该项目开发了下一代传感器融合工具，即，以最佳地联合收割机组合来自位于多个地点的传感器的信息，或者通过以非常高的频率监视单个传感器。 在这个项目中开发的算法将比大多数最先进的方法更强大和普遍适用。 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刻影响。

项目成果

Demonstration Platform for Trajectory Planning of an Autonomous Nonholonomic Skid-Steer System for Investigating Spatial Phenomena

用于研究空间现象的自主非完整滑移系统轨迹规划演示平台

• DOI: 10.2514/6.2016-0398
• 发表时间: 2016
• 期刊: (AIAA 2016-0398
• 作者: Keller, Gordon J.;Hening, Sebastian;Song, Sisi;Teodorescu, Mircea;Guillaume, Brat;Nguyen, Nhan T.;Ippolito, Corey A.
• 通讯作者: Ippolito, Corey A.

Assessing differences in legislators' revealed preferences: A case study on the 107th U.S. Senate

• DOI: 10.1214/16-aoas951
• 发表时间: 2016-01
• 期刊: arXiv: Applications
• 作者: Chelsea Lofland;Abel Rodríguez;Scott Moser

Bayesian Nonparametric Measurement of Factor Betas and Clustering with Application to Hedge Fund Returns

因子 Beta 的贝叶斯非参数测量和聚类及其在对冲基金收益中的应用

• DOI: 10.3390/econometrics4010013
• 发表时间: 2016
• 期刊: Econometrics
• 影响因子: 1.5
• 作者: Garay, Urbi;ter Horst, Enrique;Molina, German;Rodriguez, Abel
• 通讯作者: Rodriguez, Abel

Bayesian Fused Lasso Regression for Dynamic Binary Networks

动态二元网络的贝叶斯融合套索回归

• DOI: 10.1080/10618600.2017.1341323
• 发表时间: 2017
• 期刊: Journal of Computational and Graphical Statistics
• 影响因子: 2.4
• 作者: Betancourt, Brenda;Rodríguez, Abel;Boyd, Naomi
• 通讯作者: Boyd, Naomi

Investigating competition in financial markets: a sparse autologistic model for dynamic network data

调查金融市场的竞争：动态网络数据的稀疏自逻辑模型

• DOI: 10.1080/02664763.2017.1357684
• 发表时间: 2017
• 期刊: Journal of Applied Statistics
• 影响因子: 1.5
• 作者: Betancourt, Brenda;Rodríguez, Abel;Boyd, Naomi
• 通讯作者: Boyd, Naomi