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Xin Wang

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Proceedings Article | 15 August 2023 Paper

Research on model fusion algorithm based on CSI fingerprint localization

Xin Wang, Guifen Chen, Guangjiao Chen

Proceedings Volume 12719, 127194O (2023) https://doi.org/10.1117/12.2685752

KEYWORDS: Data modeling, Education and training, Machine learning, Covariance matrices, Signal processing, Tunable filters, Signal filtering, Signal attenuation, Performance modeling, Detection and tracking algorithms

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High precision indoor positioning technology is the key research content in the field of communication and control. In order to reduce the influence of environmental factors on indoor positioning accuracy, a fingerprint location model fusion algorithm based on channel state information (CSI) was proposed. In the off-line stage, the pauta criterion is used to remove abnormal data, and then the CSI amplitude is filtered by Kalman to make the CSI amplitude have certain periodic characteristics. Then the phase of CSI is corrected by linear transformation, and the processed amplitude and phase are taken as the joint CSI fingerprint. Finally, CatBoost-KNN model fusion localization algorithm was used to train fingerprint data. In the online stage, the coordinates of the pre-processed points to be measured are predicted by the trained CatBoost- KNN model. Simulation results show that the average positioning error of this method is 0.96m. Compared with some existing positioning methods, the positioning accuracy of the improved method is improved, and the algorithm has good practicability in large-scale indoor positioning applications.

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