WiFi-Based Indoor Positioning by Random Forest and Adjusted Cosine Similarity

被引：0
作者：
Gao, Jieyu [1 ]
Li, Xiaohui [1 ,3 ]
Ding, Yuemin [2 ]
Su, Qian [1 ,3 ]
Liu, Zhenxing [1 ,3 ]
机构：
[1] Wuhan Univ Sci & Technol, Sch Informat Sci & Engn, Wuhan 430081, Peoples R China
[2] Tianjin Univ Technol, Sch Comp Sci & Technol, Tianjin 300384, Peoples R China
[3] Wuhan Univ Sci & Technol, Engn Res Ctr Met Automat & Measurement Technol, Minist Educ, Wuhan 430081, Peoples R China
来源：
PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020) | 2020年
关键词：
WiFi Location Fingerprint; Random Forest; Adjusted Cosine Similarity; Regional Grid Division;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
WiFi based indoor positioning technology is very popular due to no additional hardware. WiFi location fingerprint is widely used for WiFi-based indoor positioning system. They are collected into fingerprint database to be used for estimating the location. In order to accurately estimate the indoor location, a kind of improved random forest algorithm is proposed in this paper. The proposed algorithm utilizes regional grid division to reduce the maximum error, and adopts adjusted cosine similarity to match the proper grid and fingerprint. Compared to the original random forest, the proposed algorithm reduces 1.15m in terms of maximum error.
引用
收藏
页码：1426 / 1431
页数：6
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