Wei, Xijia;
Wei, Zhiqiang;
Radu, Valentin;
(2022)
MM-Loc: Cross-sensor Indoor Smartphone Location Tracking using Multimodal Deep Neural Networks.
Presented at: 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Lloret de Mar, Spain.
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Abstract
Indoor positioning systems have been explored for decades to facilitate universal location-based services. However, complex environment conditions and sensing imperfections continue to be limiting factors to their large scale adoption. Rather than customising more ingenious solutions to handle corner cases in complex environments, we believe that a more efficient solution is to learn entirely from data with minimal engineering effort. We develop neural network based solutions for two positioning approaches, modelling Dead Reckoning with recurrent neural networks and WiFi Fingerprinting with deep neural networks. We propose a multimodal deep neural network architecture (MM-Loc) that bridges the features extracted by the modality-specific complements (sensor based and WiFi based) to join the two perspectives. We observe that this multimodal approach is better than single-modality models, and elegantly trains directly from raw data with minimal intervention.
Type: | Conference item (Paper) |
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Title: | MM-Loc: Cross-sensor Indoor Smartphone Location Tracking using Multimodal Deep Neural Networks |
Event: | 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN) |
Location: | Lloret de Mar, Spain |
Dates: | 29th November - 2nd December 2021 |
ISBN-13: | 9781665404020 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/IPIN51156.2021.9662519 |
Publisher version: | https://doi.org/10.1109/IPIN51156.2021.9662519 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
Keywords: | Indoor Localization, Multimodal Sensing, Sensor Fusion, WiFi Fingerprinting, Location Tracking, Dead Reckoning, Recurrent Neural Networks |
UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10149723 |
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