.png){kind=icon}
返回
LoRa-based outdoor localization and tracking using unsupervised symbolization
.png){kind=icon}
OA
AI
DOI：10.1016/j.iot.2023.101016.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a long-range (LoRa)-based outdoor localization and tracking method. Our method presents an unsupervised localization approach that utilizes symbolized LoRa received signal features, such as RSSI, SNR, and path loss, where each symbol represents a system state. To identify the partitioning boundaries between the symbols in time series, we employ maximum entropy partitioning. The D-Markov machine is used to construct nondeterministic finite-state automata for extracting temporal patterns. We incorporate the Chinese restaurant process for online estimation, especially in scenarios with an unbounded number of probable areas around each LoRa gateway. An adaptive trilateration approach is then used to localize the target node from the estimated ranged radii of areas. The point-wise localization data was used for time-series continuous tracking. We collected a dataset using three LoRaWAN gateways, sensor nodes powered by single-use batteries, and a Chirpstack server on a sports oval. We thoroughly evaluated the proposed method from the perspectives of localization accuracy and tracking capability. Our method outperformed state-of-the-art machine learning-driven range-based and fingerprint-based localization techniques.
Keyword:
LoRa localization
RSSI
Path loss
D-Markov machine
Chinese restaurant process
Maximum entropy partitioning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.9K
被引数:
6.9K
机构
引用论文
分享收藏分享收藏分享收藏A deep learning framework for target localization in error-prone environment.png)
INTERNET OF THINGS
INTERNET OF THINGSIF7.6
PREAI
分享收藏Hierarchical symbolic dynamic filtering of streaming non-stationary time series dataSIGNAL PROCESSINGOAAI
SIGNAL PROCESSINGIF3.6
OAAI分享收藏
查看更多内容