An Improved K-Nearest-Neighbor Indoor Localization Method Based on Spearman Distance

被引：135

作者：

Xie, Yaqin ^{[1
]}

Wang, Yan ^{[2
]}

Nallanathan, Arumugam ^{[3
]}

Wang, Lina ^{[1
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Meteorol Observat & Signal Proc, Nanjing 210044, Jiangsu, Peoples R China

[2] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Jiangsu, Peoples R China

[3] Kings Coll London, Dept Informat, London WC2R 2LS, England

来源：

IEEE SIGNAL PROCESSING LETTERS | 2016年 / 23卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Indoor localization; RSSI; Spearman distance; Wi-Fi; SEQUENCE-BASED LOCALIZATION;

D O I：

10.1109/LSP.2016.2519607

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Indoor localization based on existing Wi-Fi Received Signal Strength Indicator (RSSI) is attractive since it can reuse the existing Wi-Fi infrastructure. However, it suffers from dramatic performance degradation due to multipath signal attenuation and environmental changes. To improve the localization accuracy under the above-mentioned circumstances, an improved Spearman-distance-based K-Nearest-Neighbor (KNN) scheme is proposed. Simulation results demonstrate that our improved method outperforms the original KNN method under the indoor environment with severe multipath fading and temporal dynamics.

引用

页码：351 / 355

页数：5

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