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Abstract
Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database. However, crowdsourced samples may be annotated with erroneous locations, which raises a serious question about whether they are reliable for database construction. In this paper, we propose a cross-domain cluster intersection algorithm to weight each sample reliability. We then select those samples with higher weight to construct radio propagation surfaces by fitting polynomial functions. Furthermore, we employ an entropy-like measure to weight constructed surfaces for quantifying their different subarea consistencies and location discriminations in online positioning. Field measurements and experiments show that the proposed scheme can achieve high localization accuracy by well dealing with the sample annotation error and nonuniform density challenges.
Highlights
Fingerprinting has been extensively researched for indoor localization systems in the last decade [1,2,3,4]
It was first observed that all the surfacing schemes outperform the grid fingerprinting FGrid, which validates the effectiveness of using fitted radio propagation surfaces for localization
This paper has studied the problem of constructing radio propagation surfaces from unreliable crowdsourced samples with annotation errors
Summary
Fingerprinting has been extensively researched for indoor localization systems in the last decade [1,2,3,4]. Not collected at specified locations, crowdsourced RSS samples still need to be annotated with some location information for fingerprint database construction. Compared with the site survey, such erroneous location annotation of crowdsourced samples could lead to an inaccurate radio map and degrade the performance of fingerprinting-based localization. Besides annotation errors, another challenge lies in that crowdsourced samples may not be uniformly distributed in the whole environment. We study the indoor localization through constructing radio propagation surfaces from crowdsourced samples. A two-step positioning algorithm is proposed to first determine the belonging subarea for a test sample, and a weighted surface search is exploited to estimate its location within the subarea.
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