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arXiv · 2001.11494

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

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

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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Bryan Teague, Zhenyu Liu, Florian Meyer, Andrea Conti, Moe Z. Win. 2020-01-30. Peregrine: Network Localization and Navigation with Scalable Inference and Efficient Operation. https://arxiv.org/abs/2001.11494

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