Using Fuzzy Logic to Improve BLE Indoor Positioning System

2016 ◽  
pp. 169-177 ◽  
Author(s):  
Sérgio Onofre ◽  
Bernardo Caseiro ◽  
João Paulo Pimentão ◽  
Pedro Sousa
Keyword(s):  
Fuzzy Logic ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Positioning System

Station Deployment and Signal Filter Design for Performance Evaluation of Indoor Positioning System

2013 ◽  
Vol 722 ◽  
pp. 306-310
Author(s):  
Chih Yung Chen ◽  
Rey Chue Hwang
Keyword(s):  
Fuzzy Logic ◽  
Filter Design ◽  
Indoor Positioning ◽  
Positioning System ◽  
Data Set ◽  
Distance Data ◽  
Optimal Deployment ◽  
Logic Inference ◽  
Indoor Positioning System ◽  
Average Filter

This paper investigates enhancement of fuzzy logic indoor positioning system (FLIPS) and its effect upon the wireless station deployment. The proposed scheme adopts an average filter to improve stability in transforming received signal strength (RSS) between a sensor and stations into distance. Based on the reliable distance data set, a fuzzy logic inference engine determines precise coordinates of the sensor. In order to evaluate the optimal deployment of wireless stations, this study experiments on three different size test areas within three to eight stations. Those results provide considered analysis to develope a more efficient FLIPS.

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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