I-Corps: Positioning System in GPS-denied Environments using Euclidean TOA Ranging and Linear Matrix Inequalities

为了支持具有移动用户的大型信息系统，需要可扩展的数据传播和高效的空间查询处理，因此对移动设备的精确定位有很大的需求。只有有了这些功能，才能开发可扩展的数据传播和查询评估基础设施，用于查询、索引和路由数据集，以支持新的移动应用程序。这些需求将推动客户愿意为这些功能付费。例如，重要的客户是消防员，他们将在燃烧的建筑物中使用这项技术，利用这种定位方法来定位被困在建筑物中的受害者，这种定位方法是基于消防员群体之间的机会性通信和受害者的移动设备。在这个提案中，I-Corps团队介绍了一种新的软件，可以准确地确定移动设备的位置，即使在没有gps的环境中也是如此。提出的创新定位方法不使用超宽带或超声波收发器等特殊硬件。相反，拟议中的定位软件将在普通的智能手机和笔记本电脑上运行。没有位置信息的gps拒绝目标节点与一些具有一定定位功能的范围内移动对等节点进行机会性通信。目标节点利用目标节点和对等节点之间的数据交换，通过三种算法：欧几里得TOA（到达时间）、LMI（线性矩阵不等式）和质心算法来改进其位置估计。利用欧几里得TOA方法的近似范围，应用LMI和质心算法来提高定位精度。

Because of the critical needs for scalable data dissemination and efficient spatial query processing for supporting large-scale information systems with mobile users, there is a great demand for accurate positioning of mobile devices. Only with these capabilities, one will be able to develop scalable data dissemination and query evaluation infrastructure for querying, indexing, and routing datasets to support novel mobile applications. These needs will drive customers' willingness to pay for these capabilities. For example, important customers are firemen who will use this technology in a burning building to locate victims trapped in the building using this positioning method that is based on opportunistic communication among the group of firemen and the victim's mobile devices. In this proposal, the I-Corps team introduces new software for accurately determining the positions of mobile devices, even in GPS-denied environments. The proposed innovative method for positioning does not use special hardware as UWB or ultrasounds transceivers. Instead, the proposed positioning software will run on typical smartphones and laptops. A GPS-denied target node with no position information communicates opportunistically with a number of in-range mobile peer nodes with some positioning capabilities. Data exchanges between the target node and peer nodes are used by the target node to refine its position estimation using three algorithms: Euclidean TOA (Time of Arrival), LMI (Linear Matrix Inequalities) and barycentric algorithms. Using the approximate ranges from the Euclidean TOA method, the LMI and barycentric algorithms are then applied to improve position accuracy.