Next Article in Journal
Analysis and Use of the Emotional Context with Wearable Devices for Games and Intelligent Assistants
Next Article in Special Issue
A Device-Free Indoor Localization Method Using CSI with Wi-Fi Signals
Previous Article in Journal
Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation
Previous Article in Special Issue
ES-DPR: A DOA-Based Method for Passive Localization in Indoor Environments
Open AccessArticle

Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression

1
School of Automation and Electronic Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Space Star Technology Co., Ltd., Beijing 100086, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(11), 2508; https://doi.org/10.3390/s19112508
Received: 8 April 2019 / Revised: 21 May 2019 / Accepted: 28 May 2019 / Published: 31 May 2019
This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts to complex scenes with multipath effects or many access points (APs). More specifically, the pre-processing algorithm makes the RSSI vector which is formed by considerable RSSI values from different APs readable by the CNN algorithm. The trained CNN model improves the positioning performance by taking a series of RSSI vectors into account and extracting local features. In this design, however, the performance is to be further improved by applying the GPR algorithm to adjust the coordinates of target points and offset the over-fitting problem of CNN. After implementing the hybrid model, the model is experimented with a public database that was collected from a library of Jaume I University in Spain. The results show that the hybrid model has outperformed the model using k-nearest neighbor (KNN) by 61.8%. While the CNN model improves the performance by 45.8%, the GPR algorithm further enhances the localization accuracy. In addition, the paper has also experimented with the three kernel functions, all of which have been demonstrated to have positive effects on GPR. View Full-Text
Keywords: fingerprinting localization; received signal strength indication; k-nearest neighbor; convolutional neural network; Gaussian process regression; cumulative error distribution fingerprinting localization; received signal strength indication; k-nearest neighbor; convolutional neural network; Gaussian process regression; cumulative error distribution
Show Figures

Figure 1

MDPI and ACS Style

Zhang, G.; Wang, P.; Chen, H.; Zhang, L. Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression. Sensors 2019, 19, 2508.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop