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.

该项目旨在开发用于从被噪声和异常值破坏的高维非欧几里得数据进行分布式传感的理论和算法。 这种分布式传感框架的开发面临着几个关键挑战。 例如，大多数分布式算法（例如“共识”）通过对低维欧几里得数据进行局部平均来获得全局平均值。 然而，在大多数应用中，数据都是高维的，并且受到噪声和异常值的困扰。 此外，目标不一定是对测量结果进行平均，而是对从测量结果推断出的模型达成共识。 由于此类模型的估计通常涉及流形的优化，因此几乎所有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。 该项目基于流形上新颖的鲁棒共识算法，为分布式传感提供了重大的范式转变。 第一个目标是开发基于几何控制、图论和机器学习的分布式传感算法，用于处理与黎曼流形中的参数相关的数据。 第二个目标是开发基于稳健统计和机器学习的分布式传感算法，该算法对噪声和异常值具有鲁棒性。 第三个目标是将流形上的这些鲁棒一致性算法应用于无线传感器网络中的几个分布式定位问题。流形上的鲁棒分布式估计技术的发展可以影响许多应用领域，例如监视、安全、远程沉浸、空间探索、环境监测、居家生活辅助。 此类应用需要经过混合、嵌入式和网络系统、机器人、传感器网络、控制理论、计算机视觉和机器学习交叉领域培训的专业人员。 该团队的多学科专业知识将促进这些学科交叉的培训。