An Indoor Positioning System Based on Probabilistic Model with ZigBee Sensor Networks

2010 ◽  
Vol 14 (6) ◽  
pp. 669-676
Author(s):  
Junpei Tsuji ◽  
◽  
Hidenori Kawamura ◽  
Keiji Suzuki ◽  
Takeshi Ikeda ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Probabilistic Model ◽  
Indoor Positioning ◽  
Mobile Node ◽  
Positioning System ◽  
Multipath Effect ◽  
Zigbee Network ◽  
Node Location ◽  
Indoor Positioning System ◽  
Commercial Spaces

In large indoor commercial spaces constructed recently, services are improved if indoor locations of visitors and employees can be detected. The indoor positioning using ZigBee network based on preobservation of RSSI at positioning areas we propose presents experiments on positioning a mobile ZigBee node on a laboratory floor, including multipath effect. Using our proposal, we determine mobile node location within an accuracy of 3.0 m.

scholarly journals A high-accuracy indoor positioning system based on UWB

2018 ◽  
Vol 173 ◽  
pp. 01021
Author(s):  
Hanyu Liu ◽  
Yanhan Zeng ◽  
Ruguo Li ◽  
Huajie Huang
Keyword(s):  
Indoor Positioning ◽  
Mobile Node ◽  
High Accuracy ◽  
Base Stations ◽  
Ultra Wideband ◽  
Time Of Arrival ◽  
Positioning System ◽  
Wide Range ◽  
Fixed Base ◽  
Indoor Positioning System

In this paper, a high-accuracy indoor positioning system based on the ultra-wideband (UWB) technique is proposed. The proposed system uses a simple ranging process to obtain the distance between the mobile node and the fixed base stations. Besides, an improved time of arrival (ToA) algorithm with Kalman filtering is proposed to improve the positioning accuracy. Measurements have been performed in the real indoor 13m*7.6m environment with many obstacles and the root-mean-square error (RMSE) is less than 0.3m. The proposed system offers a wide range of application in robotics, industrial automation, post-sorting system and so on.

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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