A Bayesian Approach to Distributed Estimation for Multi-Object Systems

多目标系统分布式估计的贝叶斯方法

• 批准号: FT210100506
• 负责人: Prof Ba Tuong Vo
• 金额: $ 71.64万
• 依托单位: Curtin University
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-06-30 至 2026-06-29
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT210100506
• 关键词: Bayesian; Approach; Distributed; Estimation; Multi

This project aims to develop new signal processing techniques that facilitate autonomous technologies for environmental perception, with the ability to efficiently process large data volumes from multiple sensing modalities. Rapid advances in sensors and networks have led to a digital data deluge, from which extracting useful information presents new technological challenges and opportunities. To address this development, this project seeks to develop new distributed solutions for statistical estimation, which are specifically designed for dynamic systems with multiple object states, and are inherently scalable and robust. The potential benefits include new technologies for smart cities, autonomous infrastructure, and digital productivity.

该项目旨在开发新的信号处理技术，促进环境感知的自主技术，并能够有效地处理来自多种传感模式的大量数据。传感器和网络的快速发展导致了数字数据的泛滥，从中提取有用的信息带来了新的技术挑战和机遇。为了解决这一问题，该项目旨在开发新的分布式解决方案，用于统计估计，这是专门为具有多个对象状态的动态系统设计的，并且具有内在的可扩展性和鲁棒性。潜在的好处包括智能城市、自主基础设施和数字生产力的新技术。