Abstract
Extensible, stable and accurate indoor positioning technology is the main goal of future large-scale perception services. With the widespread deployment of wireless hotspots, the demand for location-based services is also increasing. Location fingerprint technology is one of the main localization algorithms in this field, because it does not need expensive hardware facilities and can be located through existing resources and software. With the increase of the number of wireless access points in fingerprints or the number of fingerprints in database, the complexity of location algorithm will increase, and it may be difficult to achieve location fingerprint for large-scale multi-building and multi-floor. Therefore, we introduce the relevant classification technology of integrated learning, and use the XGBoost positioning algorithm to design the classification of indoor positioning to improve the positioning accuracy and reduce the computational complexity. Finally, according to the simulation results, the performance of the algorithm is analyzed, and the localization effects of the algorithm and other localization algorithms are compared and analyzed.
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Acknowledgements
This work is supported by Heilongjiang Provincial Education Department Project (SJGY20180390), Heilongjiang University Project (2018B14), Heilongjiang University Graduate Innovation Competition (20170160903).
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Ma, H., Ma, Y., Wang, Y., Xu, X., Zhuang, W. (2020). Location Fingerprint Indoor Positioning Based on XGBoost. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_175
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DOI: https://doi.org/10.1007/978-981-13-9409-6_175
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