Indoor Location Fingerprinting Algorithm Based on Path Loss Parameter Estimation and Bayesian Inference

Huan, Hai; Wang, Kailiang; Xie, Yaqin; Zhou, Lili

doi:10.1109/jsen.2022.3227539

Cited by 8 publications

(1 citation statement)

References 34 publications

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“…Each LED light is assigned its corresponding ID information. The receiving end receives and analyzes the light signal through the photodetector to obtain the position information of each LED lamp and the received signal intensity value [4] . In the visible light communication system, the signal can be divided into direct signals and reflected signals, and in the whole VLC system the direct signal of all the largest proportion of the least, therefore, to facilitate calculation, this chapter only consider the distance of sight (LOS) link channel model, between the transmitting end and the receiving end is not affected by the barrier, the light signal from the transmitter can be directly transmitted between the receiver.…”

Section: System Modelmentioning

confidence: 99%

Indoor Visible Light Localization Algorithm Based on KNN and Bayesian Algorithm

2024

jeis

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In this paper, based on KNN and Bayesian algorithm, the basic algorithm principle of KNN (WKNN) and Bayesian is expounded. Because the KNN and Bayesian algorithm develop the signal intensity matching strategy from the perspective of mean error and probability, so one of the single algorithm can not better cope with the complex and changeable positioning scenario. For this problem, this paper proposes a new signal intensity matching criterion based on the fusion of two algorithms. The main idea of the algorithm is to change the traditional weighting method in WKNN to the weighting method considering Bayesian estimation results. In order to verify the effectiveness of the fusion algorithm, the existing visible light indoor positioning algorithm based on fingerprint recognition is compared, and the fusion algorithm based on KNN and Bayesian algorithm is proposed. This improved algorithm not only reduces the complexity of Bayesian algorithm, but also significantly improves the positioning accuracy of WKNN algorithm.

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Section: System Modelmentioning

confidence: 99%