scholarly journals TrackCC: A Practical Wireless Indoor Localization System Based on Less-Expensive Chips

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1391 ◽  
Author(s):  
Xiaolong Li ◽  
Yan Zheng ◽  
Jun Cai ◽  
Yunfei Yi
Keyword(s):  
Indoor Localization ◽  
Localization System ◽  
Wireless Indoor Localization

FineLoc: A Fine-Grained Self-Calibrating Wireless Indoor Localization System

2019 ◽  
Vol 18 (9) ◽  
pp. 2077-2090 ◽  
Author(s):  
Xinyu Tong ◽  
Ke Liu ◽  
Xiaohua Tian ◽  
Luoyi Fu ◽  
Xinbing Wang
Keyword(s):  
Indoor Localization ◽  
Localization System ◽  
Fine Grained ◽  
Wireless Indoor Localization

Design of a Bell-Shaped Ultra Wideband Antenna for Indoor Localization System

2015 ◽  
Vol 5 (6) ◽  
pp. 323 ◽  
Author(s):  
Nadia Ghariani ◽  
Mohamed Salah Karoui ◽  
Mondher Chaoui ◽  
Mongi Lahiani ◽  
Hamadi Ghariani
Keyword(s):  
Indoor Localization ◽  
Ultra Wideband ◽  
Localization System ◽  
Wideband Antenna

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

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