An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Localization

Electronics

卷:9

Weixing Xue¹  Kegen Yu²  Ruizhi Chen³  Feng Ye³  Xuesheng Peng³ 

[1] Department of Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China;

[2] School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;

[3] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;

关键词: Euclidean distance;    fingerprinting localization;    physical distance of RSS;    weighted K-nearest neighbor;   

DOI  :  10.3390/electronics9122117

来源: DOAJ

【 摘 要 】

The weighted K-nearest neighbor (WKNN) algorithm is the most commonly used algorithm for indoor localization. Traditional WKNN algorithms adopt received signal strength (RSS) spatial distance (usually Euclidean distance and Manhattan distance) to select reference points (RPs) for position determination. It may lead to inaccurate position estimation because the relationship of received signal strength and distance is exponential. To improve the position accuracy, this paper proposes an improved weighted K-nearest neighbor algorithm. The spatial distance and physical distance of RSS are used for RP selection, and a fusion weighted algorithm based on these two distances is used for position calculation. The experimental results demonstrate that the proposed algorithm outperforms traditional algorithms, such as K-nearest neighbor (KNN), Euclidean distance-based WKNN (E-WKNN), and physical distance-based WKNN (P-WKNN). Compared with the KNN, E-WKNN, and P-WKNN algorithms, the positioning accuracy of the proposed method is improved by about 29.4%, 23.5%, and 20.7%, respectively. Compared with some recently improved WKNN algorithms, our proposed algorithm can also obtain a better positioning performance.

【 授权许可】

Unknown