Next Article in Journal
Location-Based Lattice Mobility Model for Wireless Sensor Networks
Next Article in Special Issue
Integrating Moving Platforms in a SLAM Agorithm for Pedestrian Navigation
Previous Article in Journal
Natural Computing Applied to the Underground System: A Synergistic Approach for Smart Cities
Previous Article in Special Issue
Augmentation of GNSS by Low-Cost MEMS IMU, OBD-II, and Digital Altimeter for Improved Positioning in Urban Area
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle

Smartphone-Based Indoor Localization within a 13th Century Historic Building

1
Faculty of Computer Science and Business Information Systems, University of Applied Sciences Würzburg-Schweinfurt, 97074 Würzburg, Germany
2
Institute of Medical Informatics, University of Lübeck, 23538 Lübeck, Germany
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4095; https://doi.org/10.3390/s18124095
Received: 27 September 2018 / Revised: 9 November 2018 / Accepted: 17 November 2018 / Published: 22 November 2018
(This article belongs to the Special Issue Sensor Fusion and Novel Technologies in Positioning and Navigation)
  |  
PDF [2421 KB, uploaded 29 November 2018]
  |  

Abstract

Within this work we present an updated version of our indoor localization system for smartphones. The pedestrian’s position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models. Our recently presented approximation scheme of the kernel density estimation allows to find an exact estimation of the current position, compared to classical methods like weighted-average. Absolute positioning information is given by a comparison between recent Wi-Fi measurements of nearby access points and signal strength predictions. Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access points, we use an optimization scheme based on a set of reference measurements to estimate a corresponding Wi-Fi model. This work provides three major contributions to the system. The most essential contribution is the novel state transition based on continuous walks along a navigation mesh, modeling only the building’s walkable areas. The localization system is further updated by incorporating a threshold-based activity recognition using barometer and accelerometer readings, allowing for continuous and smooth floor changes. Within the scope of this work, we tackle problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures. For the latter, a simplification of our previous solution is presented for the first time, which does not involve any major changes to the particle filter. The goal of this work is to propose a fast to deploy localization solution, that provides reasonable results in a high variety of situations. To stress our system, we have chosen a very challenging test scenario. All experiments were conducted within a 13th century historic building, formerly a convent and today a museum. The system is evaluated using 28 distinct measurement series on four different test walks, up to 310 m length and 10 min duration. It can be shown, that the here presented localization solution is able to provide a small positioning error, even under difficult conditions and faulty measurements. The introduced filtering methods allow for a real fail-safe system, while the optimization scheme enables an on-site setup-time of less then 120 min for the building’s 2500 m2 walkable area. View Full-Text
Keywords: indoor localization; Wi-Fi; PDR; sensor fusion; smartphone; particle filter; sample impoverishment; estimation; historic buildings; navigation mesh indoor localization; Wi-Fi; PDR; sensor fusion; smartphone; particle filter; sample impoverishment; estimation; historic buildings; navigation mesh
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Fetzer, T.; Ebner, F.; Bullmann, M.; Deinzer, F.; Grzegorzek, M. Smartphone-Based Indoor Localization within a 13th Century Historic Building. Sensors 2018, 18, 4095.

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.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top