Fingerprint location algorithm based on K‐means for spatial farthest access point in Wi‐Fi environment. Issue 4 (11th December 2019)

• Record Type: Journal Article
• Authors: Wu, Chun‐Ming; Qi, Sen‐Nan; Zhao, Chen
• Is Part Of: Journal of engineering. Volume 2020:Issue 4(2020)
• Page Start: 115
• Page End: 119
• Publication Date: 2019-12-11
• Subjects: fingerprint identification -- computational complexity -- mobile computing -- pattern clustering -- wireless LAN; location process -- strongest access point -- location fingerprints recognition algorithm -- farthest spatial AP -- traditional K‐means algorithm -- optimal initial clustering centres -- rough location -- fingerprint position -- nearest neighbour algorithm -- location fingerprint algorithm -- clustering time -- matched fingerprints -- computational complexity -- fingerprint location algorithm -- spatial farthest access point -- Wi‐Fi environment -- huge fingerprint database -- complex location information; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2019.0995
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : The main problems of location fingerprint are the timeliness and accuracy of location. However, the huge fingerprint database and complex location information will make the location process extremely complex and time‐consuming. On the basis of introducing the basic idea of the strongest access point (AP), a location fingerprints recognition algorithm based on K‐means clustering of the farthest spatial AP of Wi‐Fi is proposed. This algorithm improves the traditional K‐means algorithm, chooses the optimal initial clustering centres based on the idea of the longest distance in space, and optimises the fingerprint database by using the improved algorithm to complete the rough location of fingerprint position. Then, the weight coefficients of AP are introduced into the Euclidean distance of the weighted k ‐nearest neighbour algorithm to enhance the contribution of spatial AP and achieve accurate location of the location fingerprint algorithm. The simulation results show the effectiveness of the algorithm. The algorithm not only effectively reduces the clustering time and the number of matched fingerprints, but also reduces the computational complexity of the algorithm, and reduces the negative impact on the real‐time and accuracy of the positioning system.