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.
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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, H., Chen, Y., Zhang, Q., Zhang, S., Ye, H., Li, XS. (2022). Research on Indoor Passive Location Based on LoRa Fingerprint. In: Jiang, X. (eds) Machine Learning and Intelligent Communications. MLICOM 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-031-04409-0_5
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DOI: https://doi.org/10.1007/978-3-031-04409-0_5
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