Version 1
: Received: 16 September 2021 / Approved: 17 September 2021 / Online: 17 September 2021 (09:43:06 CEST)
Version 2
: Received: 13 October 2021 / Approved: 13 October 2021 / Online: 13 October 2021 (12:14:39 CEST)
Wei, X.; Wei, Z.; Radu, V. Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks. Sensors2021, 21, 7488.
Wei, X.; Wei, Z.; Radu, V. Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks. Sensors 2021, 21, 7488.
Wei, X.; Wei, Z.; Radu, V. Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks. Sensors2021, 21, 7488.
Wei, X.; Wei, Z.; Radu, V. Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks. Sensors 2021, 21, 7488.
Abstract
Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localisation using smartphone sensors. However, specialising these solutions for difficult edge cases remains challenging. Here we propose an end-to-end hybrid multimodal deep neural network localisation system, MM-Loc, relying on zero hand-engineered features, learning them automatically from data instead. This is achieved by using modality-specific neural networks to extract preliminary features from each sensing modality, which are then combined by cross-modality neural structures. We show that our choice of modality-specific neural architectures is capable of estimating the location with good accuracy independently. But for better accuracy, a multimodal neural network fusing the features of early modality-specific representations is a better proposition. Our proposed MM-Loc solution is tested on cross-modality samples characterised by different sampling rates and data representation (inertial sensors, magnetic and WiFi signals), outperforming traditional approaches for location estimation. MM-Loc elegantly trains directly from data unlike conventional indoor positioning systems, which rely on human intuition.
Keywords
Indoor Localization; Sensor Fusion; Multimodal Deep Neural Network; Multimodal Sensing; WiFi Fingerprinting; Pedestrian Dead Reckoning
Subject
Computer Science and Mathematics, Computer Science
Copyright:
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.
Received:
13 October 2021
Commenter:
Xijia Wei
Commenter's Conflict of Interests:
Author
Comment:
We add a new section of the comparative study that comparing our proposed MM-Loc model performance with other state-of-the-art positioning systems.
Commenter: Xijia Wei
Commenter's Conflict of Interests: Author