scholarly journals Random Forest and Interpolation Techniques for Fingerprint-based Indoor Positioning System in Un-ideal Environment

2021 ◽  
Vol 10 (1) ◽  
pp. 701-713
Author(s):  
Dwi Joko Suroso ◽  
Alvin S. H. Rudianto ◽  
Muhammad Arifin ◽  
Singgih Hawibowo
Keyword(s):  
Random Forest ◽  
Indoor Positioning ◽  
Positioning System ◽  
Interpolation Techniques ◽  
Indoor Positioning System

scholarly journals Random Forest Based Coarse Locating and KPCA Feature Extraction for Indoor Positioning System

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yun Mo ◽  
Zhongzhao Zhang ◽  
Yang Lu ◽  
Weixiao Meng ◽  
Gul Agha
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Clustering Algorithms ◽  
Principal Component ◽  
Extraction Methods ◽  
Indoor Positioning ◽  
Test Point ◽  
Kernel Principal Component Analysis ◽  
Positioning System ◽  
Indoor Positioning System

With the fast developing of mobile terminals, positioning techniques based on fingerprinting method draw attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve the system performance, on the one hand, in the paper, we propose a coarse positioning method based on random forest, which is able to customize several subregions, and classify test point to the region with an outstanding accuracy compared with some typical clustering algorithms. On the other hand, through the mathematical analysis in engineering, the proposed kernel principal component analysis algorithm is applied for radio map processing, which may provide better robustness and adaptability compared with linear feature extraction methods and manifold learning technique. We build both theoretical model and real environment for verifying the feasibility and reliability. The experimental results show that the proposed indoor positioning system could achieve 99% coarse locating accuracy and enhance 15% fine positioning accuracy on average in a strong noisy environment compared with some typical fingerprinting based methods.

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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