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Learned Multi-Sensor Indoor Positioning of Mobile Devices

Kjellson, Christoffer LU (2021) In Master’s Theses in Mathematical Sciences FMAM05 20211
Mathematics (Faculty of Engineering)
Abstract
Attenuation of the microwave signal used in the Global Positioning System (GPS) due to interaction with building structures complicates the task of accurate indoor positioning. For this reason, research on alternative approaches is being performed at universities and companies worldwide. The Global Indoor Navigation (GIN) research project in Lund is one such initiative, where the main goal is to perform indoor positioning in a vast number of buildings globally rather than in specific buildings locally. This thesis proposes and investigates several methods and algorithms for indoor positioning with applications in the GIN project that are adaptive to different buildings, users and devices. The main contribution is a novel multi-purpose deep... (More)
Attenuation of the microwave signal used in the Global Positioning System (GPS) due to interaction with building structures complicates the task of accurate indoor positioning. For this reason, research on alternative approaches is being performed at universities and companies worldwide. The Global Indoor Navigation (GIN) research project in Lund is one such initiative, where the main goal is to perform indoor positioning in a vast number of buildings globally rather than in specific buildings locally. This thesis proposes and investigates several methods and algorithms for indoor positioning with applications in the GIN project that are adaptive to different buildings, users and devices. The main contribution is a novel multi-purpose deep learning architecture and training procedure which leverages received signal strength indications (RSSI) from Wi-Fi and Bluetooth beacons. The approach achieves a mean positioning error that is comparable with state of the art WKNN methods, offering improved inference speed. During one training session, three multi-layer perceptrons are generated, which all can be used separately in different applications. In addition to the development of this model, it is also investigated how the resulting position estimates can be combined with estimated displacements in position that are based on inertial sensors in mobile devices. This part also shows promising results, as the investigated approach decreases the mean positioning error from 7.2 m to 5.4 m for the largest dataset used. The models and algorithms were evaluated on two fingerprinting datasets, and in a Kaggle competition where these contributed to the second place entry. (Less)
Please use this url to cite or link to this publication:
author
Kjellson, Christoffer LU
supervisor
organization
course
FMAM05 20211
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Indoor Positioning, Deep Learning, Fingerprinting, RSSI, Machine Learning, Pedestrian Dead Reckoning, Kaggle, Sensor Fusion, SDAE, LSTM
publication/series
Master’s Theses in Mathematical Sciences
report number
LUTFMA-3448-2021
ISSN
1404-6342
other publication id
2021:E31
language
English
id
9054187
date added to LUP
2021-07-02 14:15:23
date last changed
2021-07-02 14:15:23
@misc{9054187,
  abstract     = {{Attenuation of the microwave signal used in the Global Positioning System (GPS) due to interaction with building structures complicates the task of accurate indoor positioning. For this reason, research on alternative approaches is being performed at universities and companies worldwide. The Global Indoor Navigation (GIN) research project in Lund is one such initiative, where the main goal is to perform indoor positioning in a vast number of buildings globally rather than in specific buildings locally. This thesis proposes and investigates several methods and algorithms for indoor positioning with applications in the GIN project that are adaptive to different buildings, users and devices. The main contribution is a novel multi-purpose deep learning architecture and training procedure which leverages received signal strength indications (RSSI) from Wi-Fi and Bluetooth beacons. The approach achieves a mean positioning error that is comparable with state of the art WKNN methods, offering improved inference speed. During one training session, three multi-layer perceptrons are generated, which all can be used separately in different applications. In addition to the development of this model, it is also investigated how the resulting position estimates can be combined with estimated displacements in position that are based on inertial sensors in mobile devices. This part also shows promising results, as the investigated approach decreases the mean positioning error from 7.2 m to 5.4 m for the largest dataset used. The models and algorithms were evaluated on two fingerprinting datasets, and in a Kaggle competition where these contributed to the second place entry.}},
  author       = {{Kjellson, Christoffer}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master’s Theses in Mathematical Sciences}},
  title        = {{Learned Multi-Sensor Indoor Positioning of Mobile Devices}},
  year         = {{2021}},
}