LoT: A Transformer-Based Approach Based on Channel State Information for Indoor Localization

Author:

Li Wei1ORCID,Meng Xiangxu2ORCID,Zhao Zheng2,Liu Zhihan2,Chen Chuhao2,Wang Huiqiang2ORCID

Affiliation:

1. College of Computer Science and Technology and the Modeling and Emulation in E-Government National Engineering Laboratory, Harbin Engineering University, Heilongjiang, Harbin, China

2. College of Computer Science and Technology, Harbin Engineering University, Heilongjiang, Harbin, China

Funder

Fundamental Research Funds for the Central Universities

China University Industry-University-Research Innovation Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Reference51 articles.

1. IDA-net: Intensity-distribution aware networks for semantic segmentation of 3D MLS point clouds in indoor corridor environments;luo;Int J Appl Earth Observ Geoinf,2022

2. A deep-learning approach for modelling pedestrian movement uncertainty in large-scale indoor areas;shi;Int J Appl Earth Observ Geoinf,2022

3. Toward 5G NR High-Precision Indoor Positioning via Channel Frequency Response: A New Paradigm and Dataset Generation Method

4. Semantic segmentation of 3D indoor LiDAR point clouds through feature pyramid architecture search

5. Target-driven visual navigation in indoor scenes using deep reinforcement learning

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1. Semi-Supervised Contrastive Learning for Few-Shot Indoor Positioning via 5G NR;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. A Novel GNN-Based Method with Light Weight Attention Augmentation for Indoor Positioning;2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT);2024-03-29

3. Novel Deep Learning-Based Approach for Indoor Localization Using Channel State Information;2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT);2024-03-29

4. EnvAwareLoc: Precision Localization Network Based on the Introduction of Environmental Information;Lecture Notes in Computer Science;2024

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