Paper
5 July 2024 Research on indoor bluetooth positioning based on transformer
Ziqian Wu, Zhiya Chen, Peilin Nie, Jinsong Fan
Author Affiliations +
Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131840E (2024) https://doi.org/10.1117/12.3032927
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia
Abstract
Diffusion neural network is a neural network model based on diffusion process, which can effectively denoise and denoise data. In addition, Transformer neural network is a neural network model based on attention mechanism, which has been widely used in natural language processing. In other fields, it can effectively learn the dependency between different positions in the input sequence and obtain the positioning coordinates. Combining the functions of the two neural networks, an indoor Bluetooth positioning method based on the Transformer neural network is proposed. This method uses the Diffusion neural network to denoise the Bluetooth positioning data to obtain high-quality positioning data; Input it into the Transformer neural network to obtain the positioning coordinates, so as to realize indoor positioning. By combining these two neural networks, the accuracy and robustness of indoor localization can be improved. The experimental results show that the denoising method based on the Diffusion neural network can effectively reduce the impact of multipath effects on the data, reduce the overall error of the data to 1 meter, and the positioning method based on the Transformer neural network can reduce the positioning error is controlled within 0-2 meters, and the average error is 1 meter.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Ziqian Wu, Zhiya Chen, Peilin Nie, and Jinsong Fan "Research on indoor bluetooth positioning based on transformer", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131840E (5 July 2024); https://doi.org/10.1117/12.3032927
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KEYWORDS
Neural networks

Diffusion

Transformers

Denoising

Machine learning

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