论文标题

二元格:具有可扩展推理和高效操作的网络定位和导航

Peregrine: Network Localization and Navigation with Scalable Inference and Efficient Operation

论文作者

Teague, Bryan, Liu, Zhenyu, Meyer, Florian, Conti, Andrea, Win, Moe Z.

论文摘要

位置感知的网络将在自动驾驶,智能城市和图像Internet等领域中启用新服务和应用程序。无处不在的本地化解决方案是网络本地化和导航(NLN),其中设备形成了一个合作本地化的网络,从而减少了准确定位所需的基础架构。本文介绍了一个名为Peregrine的实时NLN系统,该系统将分布式NLN算法与市售的超宽带(UWB)传感和通信技术相结合。第一次,第一次集成了三种NLN算法,以技术不可知的方式共同执行本地化和网络操作的任务,利用空间和时间合作。展览硬件由构成微处理器和商业UWB无线电的低成本紧凑型设备组成。本文介绍了展览系统的设计,并表征了每种算法组件的性能影响。室内实验验证了我们实现NLN的方法既可靠又可扩展,并且即使在具有挑战性的室内场景中,也可以保持次级水平的准确性。

Location-aware networks will enable new services and applications in fields such as autonomous driving, smart cities, and the Internet-of-Things. One promising solution for ubiquitous localization is network localization and navigation (NLN), where devices form a network that cooperatively localizes itself, reducing the infrastructure needed for accurate localization. This paper introduces a real-time NLN system named Peregrine, which combines distributed NLN algorithms with commercially available ultra-wideband (UWB) sensing and communication technology. The Peregrine software application, for the first time, integrates three NLN algorithms to jointly perform the tasks of localization and network operation in a technology agnostic manner, leveraging both spatial and temporal cooperation. Peregrine hardware is composed of low-cost, compact devices that comprise a microprocessor and a commercial UWB radio. This paper presents the design of the Peregrine system and characterizes the performance impact of each algorithmic component. Indoor experiments validate that our approach to realizing NLN is both reliable and scalable, and maintains sub-meter-level accuracy even in challenging indoor scenarios.

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