Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917563873624064 |
|---|---|
| author | Jiao, Jiyu Wang, Xiaojun Han, Chenpei Huang, Yuhua Zhang, Yizhuo |
| author_facet | Jiao, Jiyu Wang, Xiaojun Han, Chenpei Huang, Yuhua Zhang, Yizhuo |
| contents | While fingerprinting localization is favored for its effectiveness, it is hindered by high data acquisition costs and the inaccuracy of static database-based estimates. Addressing these issues, this letter presents an innovative indoor localization method using a data-efficient meta-learning algorithm. This approach, grounded in the ``Learning to Learn'' paradigm of meta-learning, utilizes historical localization tasks to improve adaptability and learning efficiency in dynamic indoor environments. We introduce a task-weighted loss to enhance knowledge transfer within this framework. Our comprehensive experiments confirm the method's robustness and superiority over current benchmarks, achieving a notable 23.13\% average gain in Mean Euclidean Distance, particularly effective in scenarios with limited CSI data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05711 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images Jiao, Jiyu Wang, Xiaojun Han, Chenpei Huang, Yuhua Zhang, Yizhuo Machine Learning Signal Processing While fingerprinting localization is favored for its effectiveness, it is hindered by high data acquisition costs and the inaccuracy of static database-based estimates. Addressing these issues, this letter presents an innovative indoor localization method using a data-efficient meta-learning algorithm. This approach, grounded in the ``Learning to Learn'' paradigm of meta-learning, utilizes historical localization tasks to improve adaptability and learning efficiency in dynamic indoor environments. We introduce a task-weighted loss to enhance knowledge transfer within this framework. Our comprehensive experiments confirm the method's robustness and superiority over current benchmarks, achieving a notable 23.13\% average gain in Mean Euclidean Distance, particularly effective in scenarios with limited CSI data. |
| title | Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2401.05711 |