Projects per year
Abstract
In this paper, we present a new solution to the problem of large-scale multi-building and multi-floor indoor localization based on linked neural networks, where each neural network is dedicated to a sub-problem and trained under a hierarchical stage-wise training framework. When the measured data from sensors have a hierarchical representation as in multi-building and multi-floor indoor localization, it is important to exploit the hierarchical nature in data processing to provide a scalable solution. In this regard, the hierarchical stage-wise training framework extends the original stage-wise training framework to the case of multiple linked networks by training a lower-hierarchy network based on the prior knowledge gained from the training of higher-hierarchy networks. The experimental results with the publicly-available UJIIndoorLoc multi-building and multi-floor Wi-Fi RSSI fingerprint database demonstrate that the linked neural networks trained under the proposed hierarchical stage-wise training framework can achieve a three-dimensional localization error of 7.98 m, which, to the best of the authors’ knowledge, is the most accurate result ever obtained for neural network-based models trained and evaluated with the full datasets of the UJIIndoorLoc database, and that, when applied to a model based on hierarchical convolutional neural networks, the proposed training framework can also significantly reduce the three-dimensional localization error from 11.78 m to 8.71 m. The generalization capability of the proposed framework for different localization scenarios is also demonstrated with the UTSIndoorLoc single-building and multi-floor database.
Original language | English |
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Pages (from-to) | 1-12 |
Number of pages | 12 |
Journal | IEEE Sensors Journal |
Volume | Early Access |
Issue number | Early Access |
DOIs | |
Publication status | Published - 12 Sept 2024 |
Fingerprint
Dive into the research topics of 'Hierarchical Stage-Wise Training of Linked Deep Neural Networks for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi RSSI Fingerprinting'. Together they form a unique fingerprint.Projects
- 1 Finished
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Feasibility Study of XJTLU Campus-Wide Indoor Localization System Based on Deep Neural Networks
Kim, K. S., Zhang, C., M., T., Lee, S., Li, S. & Tang, Z.
1/01/19 → 31/12/21
Project: Internal Research Project
Research output
- 1 Conference Proceeding
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Stage-Wise and Hierarchical Training of Linked Deep Neural Networks for Large-Scale Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting
Li, S., Kim, K. S., Tang, Z. & Smith, J. S., 2023, Proceedings - 2023 11th International Symposium on Computing and Networking Workshops, CANDARW 2023. Institute of Electrical and Electronics Engineers Inc., p. 63-68 6 p. (Proceedings - 2023 11th International Symposium on Computing and Networking Workshops, CANDARW 2023).Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review