Demonstration of high precision 3D indoor positioning system based on two-layer ANN machine learning technique

2019 ◽  
Author(s):  
Jiale He ◽  
Chin-Wei Hsu ◽  
Qi Zhou ◽  
Ming Tang ◽  
Songnian Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Precision ◽  
Indoor Positioning ◽  
Positioning System ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
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.

A High Precision Indoor Positioning System Based on VLC and Smart Handheld

2014 ◽  
Vol 571-572 ◽  
pp. 183-186 ◽  
Author(s):  
Zhe Xin Ren ◽  
Hong Ming Zhang ◽  
Ling Wei ◽  
Yang Guan
Keyword(s):  
Light Source ◽  
High Precision ◽  
Relative Position ◽  
Visible Light Communication ◽  
Indoor Positioning ◽  
Image Sensor ◽  
Positioning System ◽  
Led Light ◽  
Scene Image ◽  
Indoor Positioning System

This paper proposes an indoor positioning system based on visible light communication technology and intelligent terminals which equipped with image sensors, electronic compasses and gyroscope sensors. Each LED source is modulated with unique codes which represent its absolute location. The receiving terminal gets the LED’s absolute location through the optical link and capture the scene image with image sensor simultaneously. The electronic compass and the gyroscope sensor measure the terminal’s yaw angle, pitch angle and roll angle in real time, together with the scene image, relative position of the terminal and the LED light source can be calculated. Combine the absolute location of the LED light source with the relative position, the system is able to estimate the terminal’s position within the accuracy of about 2 centimeter. Position accuracy can be improved by using high-precision camera and attitude sensors.

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