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Sensors 2019, 19(8), 1790; https://doi.org/10.3390/s19081790

A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization

Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Korea
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This paper is an extended version of our paper published in “Performance Comparison of Indoor Fingerprinting Techniques Based on Artificial Neural Network”. In Proceedings of the TENCON 2018—2018 IEEE Region 10 Conference, Jeju, Korea, 28–31 October 2018.
Received: 28 February 2019 / Revised: 30 March 2019 / Accepted: 11 April 2019 / Published: 14 April 2019
(This article belongs to the Special Issue Selected Papers from TENCON 2018)
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Abstract

The performance of an Artificial Neural Network (ANN)-based algorithm is subject to the way the feature data is extracted. This is a common issue when applying the ANN to indoor fingerprinting-based localization where the signal is unstable. To date, there is not adequate feature extraction method that can significantly mitigate the influence of the receiver signal strength indicator (RSSI) variation that degrades the performance of the ANN-based indoor fingerprinting algorithm. In this work, a wavelet scattering transform is used to extract reliable features that are stable to small deformation and rotation invariant. The extracted features are used by a deep neural network (DNN) model to predict the location. The zeroth and the first layer of decomposition coefficients were used as features data by concatenating different scattering path coefficients. The proposed algorithm has been validated on real measurements and has achieved good performance. The experimentation results demonstrate that the proposed feature extraction method is stable to the RSSI variation. View Full-Text
Keywords: fingerprinting; indoor localization; indoor positioning; wavelet scattering; feature extraction fingerprinting; indoor localization; indoor positioning; wavelet scattering; feature extraction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Soro, B.; Lee, C. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 2019, 19, 1790.

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