Stochastic Geometry for Multi-sensor Data Fusion System

多传感器数据融合系统的随机几何

• 批准号: DP0345215
• 负责人: Prof Ba-Ngu Vo
• 金额: $ 18.82万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2003
• 资助国家: 澳大利亚
• 起止时间: 2003-01-01 至 2006-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0345215
• 关键词: Stochastic; Geometry; Multi; sensor; Data

The aim of this project is to develop efficient algorithms for tracking and sensor management in a multi-sensor multi-target environment. Finite random set theory provides a natural way of representing a random number of (random) object states, an issue that has been largely ignored in the tracking literature until recently. Although a satisfactory foundation for multiple object filtering has been provided by random set theory, in this early stage no algorithm capable of tracking many targets has emerged from this framework. We are confident that efficient algorithms can be developed by exploiting the insights and mathematical tools of stochastic geometry

该项目的目的是在多传感器多目标环境中开发有效的跟踪和传感器管理算法。有限随机集理论提供了一种自然的方式来表示（随机）对象状态的随机数，直到最近，跟踪文献中的一个问题在很大程度上被忽略了。虽然一个令人满意的基础，多目标过滤已提供随机集理论，在这个早期阶段，没有算法能够跟踪许多目标已经出现了从这个框架。我们有信心，有效的算法可以开发利用的见解和数学工具的随机几何