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

Research output: Contribution to journalArticlepeer-review

Yaqin Xie, Yan Wang, Arumugam Nallanathan, Lina Wang

Original languageEnglish
Pages (from-to)351-355
JournalIEEE SIGNAL PROCESSING LETTERS
Volume23
Issue number3
DOIs
PublishedMar 2016

Documents

  • Yaqin_SPL_16

    Yaqin_SPL_16.pdf, 152 KB, application/pdf

    Uploaded date:29 Feb 2016

    Version:Accepted author manuscript

King's Authors

Abstract

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.

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