Next Article in Journal
Automated Home Oxygen Delivery for Patients with COPD and Respiratory Failure: A New Approach
Previous Article in Journal
Resource Usage and Performance Trade-offs for Machine Learning Models in Smart Environments
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Sensors
Volume 20
Issue 4
10.3390/s20041177
sensors-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning

by
Joan Bordoy
1,*,†,‡,
Dominik Jan Schott
2,†,‡,
Jizhou Xie
1,‡,
Amir Bannoura
3,‡,
Philip Klein
1,‡,
Ludwig Striet
1,‡,
Fabian Hoeflinger
4,‡,
Ivo Haering
4,‡,
Leonhard Reindl
2,‡ and
Christian Schindelhauer
1,‡
1
Department of Computer Science (IIF), University of Freiburg, 79110 Freiburg, Germany
2
Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110 Freiburg, Germany
3
Department of Software Engineering, Bethlehem University, P.O. Box 11407, Bethlehem, 92248 Jerusalem, Palestine
4
Fraunhofer Institute for Highspeed Dynamics, Ernst-Mach-Institute (EMI), 79104 Freiburg, Germany
*
Author to whom correspondence should be addressed.
†
Current address: Georges-Koehler-Allee 51, 79110 Freiburg im Breisgau, Germany.
‡
All authors contributed equally to this work.
Sensors 2020, 20(4), 1177; https://doi.org/10.3390/s20041177
Received: 17 December 2019 / Revised: 31 January 2020 / Accepted: 14 February 2020 / Published: 20 February 2020
(This article belongs to the Section Sensor Networks)
View Full-Text Download PDF
Browse Figures

Abstract

An acoustic transmitter can be located by having multiple static microphones. These microphones are synchronized and measure the time differences of arrival (TDoA). Usually, the positions of the microphones are assumed to be known in advance. However, in practice, this means they have to be manually measured, which is a cumbersome job and is prone to errors. In this paper, we present two novel approaches which do not require manual measurement of the receiver positions. The first method uses an inertial measurement unit (IMU), in addition to the acoustic transmitter, to estimate the positions of the receivers. By using an IMU as an additional source of information, the non-convex optimizers are less likely to fall into local minima. Consequently, the success rate is increased and measurements with large errors have less influence on the final estimation. The second method we present in this paper consists of using machine learning to learn the TDoA signatures of certain regions of the localization area. By doing this, the target can be located without knowing where the microphones are and whether the received signals are in line-of-sight or not. We use an artificial neural network and random forest classification for this purpose. View Full-Text
Keywords: self-calibration; localization; ultrasound; machine learning; indoor localization; tdoa; random forest self-calibration; localization; ultrasound; machine learning; indoor localization; tdoa; random forest
►▼ Show 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
SciFeed

Share and Cite

MDPI and ACS Style

Bordoy, J.; Schott, D.J.; Xie, J.; Bannoura, A.; Klein, P.; Striet, L.; Hoeflinger, F.; Haering, I.; Reindl, L.; Schindelhauer, C. Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning. Sensors 2020, 20, 1177. https://doi.org/10.3390/s20041177

AMA Style

Bordoy J, Schott DJ, Xie J, Bannoura A, Klein P, Striet L, Hoeflinger F, Haering I, Reindl L, Schindelhauer C. Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning. Sensors. 2020; 20(4):1177. https://doi.org/10.3390/s20041177

Chicago/Turabian Style

Bordoy, Joan, Dominik J. Schott, Jizhou Xie, Amir Bannoura, Philip Klein, Ludwig Striet, Fabian Hoeflinger, Ivo Haering, Leonhard Reindl, and Christian Schindelhauer. 2020. "Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning" Sensors 20, no. 4: 1177. https://doi.org/10.3390/s20041177

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop