An Improved Weighted K Nearest Neighbors Algorithm for WLAN Localization

Abstract:

For indoor location estimation based on Wi-Fi fingerprinting, how to reduce localization effort while maintaining high location estimate accuracy is of critical concern. The paper introduces a new approach Reward and Penalty Localization (RP-Loc) algorithm, which extends the classic WKNN Localization algorithm. The parameter of observable Access Point (AP) occurrence rate is added into the fingerprint database in the offline training phrase to increase the increase the anti-interference ability. The experimental results prove that the RP-Loc algorithm exhibits superior performance in terms of location accuracy and robustness.

• Periodical: Advanced Materials Research (Volumes 753-755)
• Pages: 2191-2195
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.753-755.2191
• Online since: August 2013
• Authors: Ming Ye*, Yi Ke Chen, Dun Bing Tang
• Keywords: Access Point (AP), Indoor Location Estimation, Weighted k Nearest Neighbors (WKNN), Wireless Local Area Network (WLAN)

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