Application of KPCA-ESN Method in Wi-Fi Based Indoor Positioning

Authors

Li Jun, College of Automation and Electrical Engineering, Lanzhou Jiao Tong University, Lanzhou 730070, China;
Chen Ying, College of Automation and Electrical Engineering, Lanzhou Jiao Tong University, Lanzhou 730070, China;

Abstract

Abstract: Aiming at the problem that the positioning accuracy is affected by the dynamic indoor environment and time-varying received signal strength (RSS) values, a Wi-Fi based indoor positioning method using kernel principal component Analysis (KPCA) and echo state networks (ESN) is proposed. The KPCA method is used to preprocess the RSS fingerprints effectively and extract the nonlinear principal components of the inputs of the model. On the basis of KPCA, the extracted principal components are taken as the inputs to the ESN network, the nonlinear mapping between corresponding positioning features and physical locations is then established by the ESN. The proposed KPCA-ESN method is then applied to Wi-Fi based indoor positioning instances by simulation and physical environment experiments. Compared with the other positioning methods under the same condition, experimental results confirm that the proposed method has higher positioning accuracy, and can also automatically timely adapt to environmental dynamics.

Recommended Citation

Jun, Li and Ying, Chen (2020) "Application of KPCA-ESN Method in Wi-Fi Based Indoor Positioning," Journal of System Simulation: Vol. 29: Iss. 12, Article 15.
DOI: 10.16182/j.issn1004731x.joss.201712015
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss12/15

First Page

3042

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201712015

Last Page

3050

CLC

TP391.4

Recommended Citation

Li Jun, Chen Ying. Application of KPCA-ESN Method in Wi-Fi Based Indoor Positioning[J]. Journal of System Simulation, 2017, 29(12): 3042-3050.

DOI

10.16182/j.issn1004731x.joss.201712015