NeTS-NOSS: SensorNet Architectures for Indoor Location Detection: From Resolution to Robustness

提案编号：0435312PI: David Starobinski机构：Boston University题目：室内位置检测的传感器网络架构：从分辨率到鲁棒性摘要：准确鲁棒地确定传感器或目标位置的能力是传感器网络运行的基础。在开放的户外环境中，这可以通过全球定位系统（GPS）使用的三边测量技术进行。然而，在室内或密集的城市环境中，这种技术面临各种各样的衰弱并发症，包括信号反射、遮挡和不可预测的依赖性（例如，门的打开和关闭）。该项目正在研究室内位置检测系统的基本性能限制，并旨在原型传感器网络架构接近这些限制。这项工作的重点是在鲁棒性和这种系统的分辨率之间的权衡，借鉴了编码理论，大偏差和数学规划方面的文献。与GPS的发展平行，该项目的成果可以作为广泛应用的基础技术，包括跟踪共享设备，受害者灾难救援或盲人室内导航

Proposal Number: 0435312PI: David Starobinski Institution: Boston University Title: SensorNet Architectures of Indoor Location Detection: From Resolution to RobustnessAbstract: The ability to accurately and robustly determine the location of sensors or targets is fundamental to the operation of sensor networks. In open outdoor settings, this can be performed with trilateration techniques used by the Global Positioning System (GPS). However, in indoor or dense urban environments, such techniques face a wide variety of debilitating complications, including signal reflections, occlusions, and unpredictable dependencies (e.g., doors opening and closing). This project is investigating fundamental performance limits of indoor location detection systems and aims at prototyping sensor network architectures that approach these limits. The work focuses on tradeoffs between robustness and resolution of such systems, drawing on the literature in coding theory, large deviations, and mathematical programming. Paralleling the development of GPS, the results of this project could serve as a cornerstone technology for a wide variety of applications, including tracking of shared equipment, victim disaster rescue, or indoor navigation for the blind