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...

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Vydané v:	Neurocomputing (Amsterdam) Ročník 194; s. 279 - 287
Hlavní autori:	Zhang, Wei, Liu, Kan, Zhang, Weidong, Zhang, Youmei, Gu, Jason
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 19.06.2016
Predmet:	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
On-line prístup:	Získať plný text
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