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fine-grained grid computing model for Wi-Fi indoor localization in complex environments

Journal of Electronic Science and Technology 2024年第1期22卷 42-52页

作者： Yan Liang Song Chen Xin Dong Tu Liu School of Computer Engineering Chengdu Technological UniversityChengdu611730China HAN Networks Corporation Limited Beijing100094China

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor ***,the construction of the fingerprint database is quite *** when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is *** appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)*** this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization *** points(RPs)are generated in the grid by the FGGC ***,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex *** a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous *** proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy *** mean error of the proposed model is 0.36 m,far lower than that of previous ***,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor *** also shows high-accuracy performance with a fast running speed even under a large-size *** results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

关键词： fine-grained grid computing (FGGC) Indoor localization Path loss Random forest Reference points(RPs)

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