Journal
NEUROCOMPUTING
Volume 194, Issue -, Pages 279-287Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2016.02.055
Keywords
Wireless positioning; Deep Neural Networks (DNNs); Hidden Markov model (HMM); Deep Learning; Stacked Denoising Autoencoder (SDA)
Categories
Funding
- NSFC [61203253, 61573222, 61233014]
- Major Research Program of Shandong Province [2015ZDXX0801A02]
- Outstanding Young Scientist Award of Shandong Province [BS2013DX023]
- Open Program of Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing [3DL201502]
- Key Lab of ICSP MOE China
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In this paper, we propose a wireless positioning method based on Deep Learning. To deal with the variant and unpredictable wireless signals, the positioning is casted in a four-layer Deep Neural Network (DNN) structure pre-trained by Stacked Denoising Autoencoder (SDA) that is capable of learning reliable features from a large set of noisy samples and avoids hand-engineering. Also, to maintain the temporal coherence, a Hidden Markov Model (HMM)-based fine localizer is introduced to smooth the initial positioning estimate obtained by the DNN-based coarse localizer. The data required for the experiments is collected from the real world in different periods to meet the actual environment. Experimental results indicate that the proposed system leads to substantial improvement on localization accuracy in coping with the turbulent wireless signals. (C) 2016 Elsevier B.V. All rights reserved.
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