Cooperative Tracking in Harsh Environments: Statistical Framework and Network Experimentation

恶劣环境中的合作跟踪：统计框架和网络实验

• 批准号: 0901034
• 负责人: Moe Win
• 金额: $ 33万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0901034&HistoricalAwards=false
• 关键词: Cooperative; Tracking; Harsh; Environments; Statistical

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objectives of this research are to: (1) gain a fundamental understanding of the theoretical and practical benefits of cooperation for self-tracking; and (2) demonstrate the feasibility and real-world performance gains of cooperative self-tracking in a practical network setting. The approach is to first derive fundamental performance bounds for cooperative self-tracking networks. This is followed by the development of practical self-tracking and distributed algorithms that account for performance and complexity trade-offs. Finally, the newly developed algorithms are implemented in a unique ultra wideband network test bed.The intellectual merit of this research includes the potential to advance the understanding of key issues of location-aware networks. The project consists of both theoretical and experimental components and is interdisciplinary. This offers the potential to lead to novel methods and techniques to solve self-tracking problems in a wider range of environments and under more stringent requirements compared to what is currently available.The potential broader impacts of this project include commercialization and dissemination of this technology. The project has engaged with state government agencies. The research results are integrated into graduate courses at MIT, as well as in tutorials and short courses offered elsewhere. Furthermore, results and measurement data are to be made publicly available via the Internet. To advocate diversity and outreach, the project is used to host students from both MIT's Undergraduate Research Opportunities Program (UROP) and MIT's Summer Research Program (MSRP).

该奖项根据《2009 年美国复苏与再投资法案》（公法 111-5）提供资助。本研究的目的是：(1) 基本了解自我追踪合作的理论和实践效益； (2) 展示实际网络环境中协作自跟踪的可行性和实际性能增益。 该方法是首先推导协作自跟踪网络的基本性能界限。 接下来是实用的自跟踪和分布式算法的开发，这些算法考虑了性能和复杂性的权衡。 最后，新开发的算法在独特的超宽带网络测试台中实现。这项研究的智力价值包括有可能促进对位置感知网络关键问题的理解。 该项目由理论和实验部分组成，并且是跨学科的。 与目前可用的技术相比，这有可能带来新的方法和技术，以在更广泛的环境和更严格的要求下解决自我跟踪问题。该项目的潜在更广泛影响包括该技术的商业化和传播。 该项目已与州政府机构合作。 研究成果被纳入麻省理工学院的研究生课程以及其他地方提供的教程和短期课程中。 此外，结果和测量数据将通过互联网公开。 为了倡导多样性和外展性，该项目用于接待来自麻省理工学院本科生研究机会计划 (UROP) 和麻省理工学院夏季研究计划 (MSRP) 的学生。