Highly accurate 3D wireless indoor positioning system using white LED lights

2014 ◽  
Vol 50 (11) ◽  
pp. 828-830 ◽  
Author(s):  
U. Nadeem ◽  
N.U. Hassan ◽  
M.A. Pasha ◽  
C. Yuen
Keyword(s):  
Indoor Positioning ◽  
Positioning System ◽  
White Led ◽  
Led Lights ◽  
Indoor Positioning System

scholarly journals Indoor Positioning System using LED Lights and a Dual Image Sensor

2015 ◽  
Vol 19 (6) ◽  
pp. 586-591 ◽  
Author(s):  
Myoung-geun Moon ◽  
Su-il Choi ◽  
Jaehyung Park ◽  
Jin Young Kim
Keyword(s):  
Indoor Positioning ◽  
Image Sensor ◽  
Positioning System ◽  
Dual Image ◽  
Led Lights ◽  
Indoor Positioning System

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

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