Cooperative Tracking in Harsh Environments: Statistical Framework and Network Experimentation

点击翻译按钮获取中文摘要

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).