基于KFCM-LMC-LSSVM算法的WLAN室内定位方法

doi:10.3969/j.issn.1000-1158.2018.04.22

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Abstract To solve the problems of large workload whe the fingerprint database is constructed and low positioning accuracy in WLAN indoor positioning system. An indoor positioning algorithm was presented, which integrates kernelized fuzzy C-means(KFCM), low rank matrix completion (LMC) and least squares support vector machine(LSSVM). Firstly, the KFCM is used to cluster the fingerprint points, the test points are sorted into one of the small areas. According to the LMC theory, the low rank fingerprint database into high density fingerprint database was translated. Finally the position of the test points is determined by the LSSVM algorithm. Experiments showed that the KFCM-LMC-LSSVM algorithm not only reduces the workload of building the fingerprint library, but also has higher positioning accuracy.

Key words： metrology indoor positioning accuracy kernelized fuzzy C-means low-rank matrix completion inexact augmented lagrange multipliers

Received: 25 July 2017 Published: 06 July 2018

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TB973

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	Articles by authors
	WANG Yu-jie
	WANG Yuan
	ZHANG Yong

Cite this article:

WANG Yu-jie,WANG Yuan,ZHANG Yong. Indoor Positioning Algorithm for WLAN Based on KFCM-LMC-LSSVM[J]. Acta Metrologica Sinica, 2018, 39(4): 554-558.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2018.04.22 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2018/V39/I4/554

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