Collaborative Research: CSR-EHCS(EHS), TM: Distributed Sensing via Robust Consensus on Manifolds

合作研究：CSR-EHCS(EHS)，TM：通过流形上的鲁棒共识进行分布式传感

• 批准号: 0834470
• 负责人: Rene Vidal
• 金额: $ 48.75万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0834470&HistoricalAwards=false
• 关键词: Collaborative; Research; CSR; EHCS; EHS

This project aims to develop theory and algorithms for distributed sensing from high-dimensional, non-Euclidean data corrupted with noise and outliers. The development of such a distributed sensing framework faces several critical challenges. For instance, most distributed algorithms such as "consensus" proceed by locally averaging low-dimensional Euclidean data to obtain a global average. In most applications, however, data are high-dimensional, and plagued with noise and outliers. Moreover, the goal is not necessarily to average the measurements, but to reach a consensus on a model inferred from the measurements. Since the estimation of such models often involves optimization on manifolds, nearly all algorithms for solving these problems are centralized, and require resources not available in wireless sensor nodes. This project offers a significant paradigm shift in distributed sensing based on novel robust consensus algorithms on manifolds. The first goal is to develop distributed sensing algorithms based on geometric control, graph theory, and machine learning, for processing data related by parameters lying in Riemannian manifolds. The second goal is to develop distributed sensing algorithms based on robust statistics and machine learning, that are robust to noise, and outliers. The third goal is to apply these robust consensus algorithms on manifolds to several distributed localization problems in wireless sensor networks.The development of robust distributed estimation techniques on manifolds can impact many application areas, such as surveillance, security, tele-immersion, space exploration, environmental monitoring, and assisted home living. Such applications require professionals trained at the intersection of hybrid, embedded, and networked systems, robotics, sensor networks, control theory, computer vision and machine learning. The multidisciplinary expertise of the team will foster training at the intersection of these disciplines.

该项目旨在开发理论和算法，以从高维，非欧国人数据中分布得传感，这些数据被噪声和异常值损坏。 这样的分布式传感框架的发展面临着几个关键挑战。 例如，大多数分布式算法（例如“共识”）通过局部平均低维欧几里得数据来获得全球平均水平来进行。 但是，在大多数应用中，数据是高维的，并且困扰着噪声和异常值。 此外，目标不一定是平均测量值，而是要从测量值推断的模型达成共识。 由于对此类模型的估计通常涉及对流形的优化，因此几乎所有用于解决这些问题的算法都是集中式的，并且需要无线传感器节点中不可用的资源。 该项目基于新型的稳健共识算法在流形上提供了显着的分布传感范式转移。 第一个目标是基于几何控制，图理论和机器学习开发分布式传感算法，以处理通过Riemannian歧管中的参数相关的数据。 第二个目标是基于稳健的统计量和机器学习，对噪声和离群值的强大开发分布式传感算法。 第三个目标是将这些强大的共识算法应用于多种多样的无线传感器网络中的几个分布式本地化问题。在歧管上强大的分布式估计技术的开发可能会影响许多应用领域，例如监视，安全性，安全性，电视，远程接入，太空探索，环境监测，辅助监控以及辅助的家庭生活。 此类应用需要在混合，嵌入式和网络系统，机器人技术，传感器网络，控制理论，计算机视觉和机器学习的交汇处进行培训的专业人员。 团队的多学科专业知识将在这些学科的交叉点培养培训。