Information Fusion for Tracking Objects in Large-Scale Sensor Network

大规模传感器网络中跟踪物体的信息融合

• 批准号: DE210101181
• 负责人: Dr Amirali Khodadadian Gostar
• 金额: $ 28.4万
• 依托单位: RMIT University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE210101181
• 关键词: Information; Fusion; Tracking; Objects; Large

This project aims to develop a mathematical framework to combine multi-modal information coming from multiple sensors. These mobile sensors will be spatially distributed over a large-scale area for the purpose of multi-object tracking. The main application of this framework is for cooperative perception for intelligent decision making. Expected outcomes include a novel technique to integrate receiving information from multiple mobile agents (e.g. vehicle) to enhance their ability to anticipate situations in dynamic environments and to act effectively to enhance safety. This should provide benefits for the development of cooperative autonomous driving to enhance road safety.

该项目旨在开发一个数学框架来结合来自多个传感器的多模态信息。这些移动传感器将空间分布在大范围区域内，以实现多目标跟踪。该框架的主要应用是用于智能决策的协作感知。预期成果包括一种新技术，可以集成从多个移动代理（例如车辆）接收信息，以增强其预测动态环境中情况并采取有效行动以提高安全性的能力。这应该有利于协同自动驾驶的发展，以提高道路安全。