Deep Neural Networks for wireless localization in indoor and outdoor environments

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 re...

Full description

Saved in:
Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 194; pp. 279 - 287
Main Authors: Zhang, Wei, Liu, Kan, Zhang, Weidong, Zhang, Youmei, Gu, Jason
Format: Journal Article
Language:English
Published: Elsevier B.V 19.06.2016
Subjects:
Computational fluid dynamics
Deep Learning
Deep Neural Networks (DNNs)
Hidden Markov model (HMM)
Learning
Localization
Neural networks
Outdoor
Position (location)
Stacked Denoising Autoencoder (SDA)
Turbulence
Wireless communication
Wireless positioning
Stacked Denoising Autoencoder (SDA)
Hidden Markov model (HMM)
Deep Neural Networks (DNNs)
Deep Learning
Wireless positioning
ISSN:0925-2312, 1872-8286
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2016.02.055