The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 557–563, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-557-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 557–563, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-557-2020

  06 Aug 2020

06 Aug 2020

GEOMAGNETIC FIELD-BASED INDOOR POSITIONING USING BACK-PROPAGATION NEURAL NETWORKS

K. Y. Qiu1,2, H. Huang1, and A. El-Rabbany2 K. Y. Qiu et al.
  • 1School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, 102616 Daxing District, Beijing, China
  • 2Geomatics Engineering Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, Canada

Keywords: geomagnetism, indoor positioning, spectral clustering, back propagation neural networks, fingerprint database

Abstract. High-precision indoor positioning in complex environments has always been a hot research topic within the positioning and robotic communities. As one of the indoor positioning technologies, geomagnetic positioning is receiving widespread attention due to its global coverage. Additionally, geomagnetic positioning does not require special infrastructure configuration, its hardware cost is low, and its positioning errors do not accumulate over time. However, geomagnetic positioning is prone to mismatching, which causes serious problems at the positioning points. To tackle this challenge, this paper proposes an indoor localization method based on spectral clustering and weighted back-propagation neural network. The main research contribution is that in the offline phase, the spatial specificity of geomagnetism is used to define the similarity between fingerprints. In addition, a clustering-based reference point algorithm is proposed to divide the sub-fingerprint database, and a positioning prediction model based on back-propagation neural network is trained. Subsequently, in the online stage, the weights of different positioning prediction models are calculated according to the defined fingerprint similarity, weighted average prediction coordinates are obtained, and thereby the positioning accuracy is improved. Experimental results show that, in comparison with other neural network-based positioning methods, the positioning error of our proposed algorithm is reduced by approximately 26.6% and the positioning time is reduced by 24.7%. Experimental results show that the average positioning error of the algorithm is 1.81m.