Abstract
Ultra-wideband (UWB) technology offers considerable advantages for indoor positioning. However, its accuracy significantly decreases in non-line-of-sight environments, particularly in dynamic scenarios with frequent human movements. To address this challenge, this study proposed a temporal convolutional network with a channel attention module (TCN-CAM) to enhance positioning performance. The TCN architecture, employing causal and dilated convolutions, effectively mitigates the vanishing gradient problem commonly encountered in neural networks and improves the model’s capacity to capture long-range dependencies in time-series data. Concurrently, CAM enhances model adaptability by emphasizing salient features under complex conditions. Simulation and field experiments demonstrated that the TCN-CAM algorithm achieved high positioning accuracy and stability with a mean error of only 3.32 cm. Compared with LSTM-AM, CNN-CAM, and conventional TCN algorithms, the proposed method improved positioning accuracy by 76.12%, 25.06%, and 19.42%, respectively, thereby significantly enhancing the robustness and performance of UWB-based positioning systems.
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Funding
This research was funded by the Fundamental Research Funds for the Universities of Henan Province (Grant Number NSFRF230405), Doctoral Scientific Fund Project of Henan Polytechnic University (Grant Number B2017-10), Henan Polytechnic University Funding Plan for Young Backbone Teachers (Grant Number 2022XQG-08), Henan Province Science and Technology Research Projects (Grant Number: 242102320070), National Natural Science Foundation of China (Grant Number 42374029), and Henan Polytechnic University Surveying and Mapping Science and Technology “Double First-Class” Discipline Creation Project (Grant Number: CHXKYXBS05).
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Conceptualization, L.H. and Z.L.; methodology, L.H.; software, C.Z.; validation, L.H., Y.T., and H.C.; formal analysis, L.H.; investigation, H.C.; resources, Z.L.; data curation, Z.L.; writing—original draft preparation, L.H.; writing—review and editing, Y.T. and M.A.; visualization, Y.T.; supervision, Z.L.; project administration, H.C.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.
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He, L., Lian, Z., Núñez-Andrés, M.A. et al. Application of a temporal convolutional network algorithm fused with channel attention module for UWB indoor positioning. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35802-0
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DOI: https://doi.org/10.1038/s41598-026-35802-0
