Research Article
Research on Indoor Passive Location Based on LoRa Fingerprint
@INPROCEEDINGS{10.1007/978-3-031-04409-0_5, author={Heng Wang and Yuzhen Chen and Qingheng Zhang and Shifan Zhang and Haibo Ye and Xuan-Song Li}, title={Research on Indoor Passive Location Based on LoRa Fingerprint}, proceedings={Machine Learning and Intelligent Communications. 6th EAI International Conference, MLICOM 2021, Virtual Event, November 2021, Proceedings}, proceedings_a={MLICOM}, year={2022}, month={5}, keywords={LoRa RSSI Passive positioning GaussianNB}, doi={10.1007/978-3-031-04409-0_5} }- Heng Wang
Yuzhen Chen
Qingheng Zhang
Shifan Zhang
Haibo Ye
Xuan-Song Li
Year: 2022
Research on Indoor Passive Location Based on LoRa Fingerprint
MLICOM
Springer
DOI: 10.1007/978-3-031-04409-0_5
Abstract
Indoor positioning based on signal fingerprint has always been a hot research topic. But most research requires the object or person to be positioned to carry a positioning device, which is not applicable in some special scenarios. This paper selects LoRa (Long Range) as the research target and proposes an indoor passive positioning system based on LoRa fingerprint. We design and implement the signal sent from the LoRa node devices to the LoRa gateway device and get the RSSI of the nodes, also send it to the proxy server for receiving and processing. In the data processing stage, the difference-limiting filtering algorithm is used to eliminate abnormal data, and the GaussianNB (Gaussian-Naive Bayes) algorithm is used to learn and train the model. Through experiments, the accuracy rates of the two-class and multi-class prediction in the range of 3m are 97.1% and 95.5%, respectively, which verifies the feasibility of applying LoRa signal to indoor passive positioning.
