Effective Fusion of Spatially and Temporally Sparse Data for Maritime Autonomous Systems

海上自治系统时空稀疏数据的有效融合

• 批准号: 2453506
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2453506
• 关键词: Effective; Fusion; Spatially; Temporally; Sparse

Maritime Autonomous Systems (MAS) add to the opportunity to gather data from a wide range of sources that can span a large geographic, depth/altitude and with different levels of currency. Fusing and presenting that data to allow a human operator to make informed decisions is challenging. There are challenges associated with both the technical approaches to fusing the data, and human factors of its presentation to form the basis for human and machine decision making (at the tactical, operational and strategic levels). This research will address the problem of 'fusing' data, information and knowledge from disparate data sources to enable better and more consistent decision making directed towards Maritime Autonomous Systems.

海事自主系统（MAS）增加了从广泛的来源收集数据的机会，这些来源可以跨越大的地理，深度/高度和不同的货币水平。融合和呈现这些数据以允许人类操作员做出明智的决策是具有挑战性的。存在与融合数据的技术方法相关的挑战，以及其呈现的人为因素，以形成人类和机器决策的基础（在战术，作战和战略层面）。这项研究将解决“融合”来自不同数据源的数据、信息和知识的问题，以实现针对海事自主系统的更好、更一致的决策。