Experimental Demonstration of an Indoor Localization System with Single Channel Imaging Receiver

2012 ◽  
Author(s):  
KE WANG ◽  
Ampalavanapillai Nirmalathas ◽  
Christina Lim ◽  
Efstratios Skafidas
Keyword(s):  
Indoor Localization ◽  
Single Channel ◽  
Experimental Demonstration ◽  
Localization System ◽  
Imaging Receiver

Optical Wireless-Based Indoor Localization System Employing a Single-Channel Imaging Receiver

2016 ◽  
Vol 34 (4) ◽  
pp. 1141-1149 ◽  
Author(s):  
Ke Wang ◽  
Ampalavanapillai Nirmalathas ◽  
Christina Lim ◽  
Kamal Alameh ◽  
Efstratios Skafidas
Keyword(s):  
Indoor Localization ◽  
Single Channel ◽  
Optical Wireless ◽  
Localization System ◽  
Imaging Receiver

scholarly journals MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1645 ◽  
Author(s):  
Ryota Kimoto ◽  
Shigemi Ishida ◽  
Takahiro Yamamoto ◽  
Shigeaki Tagashira ◽  
Akira Fukuda
Keyword(s):  
Large Scale ◽  
Indoor Localization ◽  
Single Channel ◽  
Sensor Nodes ◽  
Localization Problem ◽  
Sensor Localization ◽  
Localization Accuracy ◽  
Localization System ◽  
Channel Diversity ◽  
Radio Signals

The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans.

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.

Acoustic indoor-localization system for smart phones

2014 ◽  
Author(s):  
Fabian Hoflinger ◽  
Joachim Hoppe ◽  
Rui Zhang ◽  
Alexander Ens ◽  
Leonhard Reindl ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Smart Phones ◽  
Localization System
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