WLAN Indoor Localization Algorithm Based on Manifold Interpolation Database Construction

ZHOU Mu; TANG Yunxia; TIAN Zengshan; WEI Yacong

doi:10.11999/JEIT161269

Volume 39 Issue 8

Aug. 2017

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ZHOU Mu, TANG Yunxia, TIAN Zengshan, WEI Yacong. WLAN Indoor Localization Algorithm Based on Manifold Interpolation Database Construction[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1826-1834. doi: 10.11999/JEIT161269

Citation:

ZHOU Mu, TANG Yunxia, TIAN Zengshan, WEI Yacong. WLAN Indoor Localization Algorithm Based on Manifold Interpolation Database Construction[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1826-1834. doi: 10.11999/JEIT161269

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WLAN Indoor Localization Algorithm Based on Manifold Interpolation Database Construction

doi: 10.11999/JEIT161269

1.
2.
(Chongqing Key Laboratory of Mobile Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

Funds:

The National Natural Science Foundation of China (61301126), The Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), The Special Fund of Chongqing Key Laboratory (CSTC), Young Scientific Research Program of CUPT (A2013-31)

Received Date: 2016-11-24
Rev Recd Date: 2017-03-20
Publish Date: 2017-08-19

Abstract

Abstract

To deal with the high cost involved in the location fingerprint database construction due to the dense Reference Points (RPs) distribution and point-by-point Received Signal Strength (RSS) collection in the conventional Wireless Local Area Network (WLAN) indoor localization systems, a new database construction approach based on the integrated semi-supervised manifold learning and cubic spline interpolation is proposed. The proposed approach utilizes a small amount of labeled data and a massive amount of unlabeled data to find the optimal solution to localization target function, and meanwhile relies on the mapping relations between the high-dimensional signal strength space and low-dimensional physical location space to calibrate the unlabeled data with location coordinates. The extensive experiments demonstrate that the proposed approach is able to guarantee the high localization accuracy, as well as significantly reduce the cost involved in location fingerprint database construction.
- Wireless Local Area Network (WLAN),
- Location fingerprint,
- Semi-supervised learning,
- Manifold alignment,
- Cubic spline interpolation

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References(26)

References

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