Open Wireless Positioning System: A Wi-Fi-Based Indoor Positioning System

Author(s):  
Matteo Cypriani ◽  
Frederic Lassabe ◽  
Philippe Canalda ◽  
Francois Spies
2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Jaime Duque Domingo ◽  
Carlos Cerrada ◽  
Enrique Valero ◽  
J. A. Cerrada

This work presents a newIndoor Positioning System(IPS) based on the combination ofWiFi Positioning System(WPS) anddepth maps, for estimating the location of people. The combination of both technologies improves the efficiency of existing methods, based uniquely on wireless positioning techniques. While other positioning systems force users to wear special devices, the system proposed in this paper just requires the use ofsmartphones, besides the installation of RGB-D sensors in the sensing area. Furthermore, the system is not intrusive, being not necessary to know people’s identity. The paper exposes the method developed for putting together and exploiting both types of sensory information with positioning purposes: the measurements of the level of the signal received from different access points (APs) of the wireless network and thedepth mapsprovided by the RGB-D cameras. The obtained results show a significant improvement in terms of positioning with respect to common WiFi-based systems.


2020 ◽  
Vol 200 ◽  
pp. 176-187
Author(s):  
Shaojie Zhuang ◽  
Jarissa Maselyne ◽  
Annelies Van Nuffel ◽  
Jürgen Vangeyte ◽  
Bart Sonck

2017 ◽  
Vol 110 ◽  
pp. 182-189 ◽  
Author(s):  
Pedro E. Lopez-de-Teruel ◽  
Felix J. Garcia ◽  
Oscar Canovas ◽  
Ruben Gonzalez ◽  
Jose A. Carrasco

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo

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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