AUTHOR=Bai Yu-Ting , Jia Wei , Jin Xue-Bo , Su Ting-Li , Kong Jian-Lei TITLE=Location estimation based on feature mode matching with deep network models JOURNAL=Frontiers in Neurorobotics VOLUME=17 YEAR=2023 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2023.1181864 DOI=10.3389/fnbot.2023.1181864 ISSN=1662-5218 ABSTRACT=Introduction

Global navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) signal outages. This paper proposes a location estimation only with inertial measurements.

Methods

A method is designed based on deep network models with feature mode matching. First, a framework is designed to extract the features of inertial measurements and match them with deep networks. Second, feature extraction and classification methods are investigated to achieve mode partitioning and to lay the foundation for checking different deep networks. Third, typical deep network models are analyzed to match various features. The selected models can be trained for different modes of inertial measurements to obtain localization information. The experiments are performed with the inertial mileage dataset from Oxford University.

Results and discussion

The results demonstrate that the appropriate networks based on different feature modes have more accurate position estimation, which can improve the localization accuracy of pedestrians in GPS signal outages.