A novel 3D indoor localization method integrating deep spatial feature augmentation and attention-based denoising

The complexity of indoor environments and the high-dimensional, diverse nature of localization data pose significant challenges for three-dimensional (3D) indoor positioning systems. Existing methods often suffer from low positioning accuracy when training data is scarce, poor robustness to noise, a...

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Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 33025 - 16
Main Authors:	Liu, Chunyuan, Yu, Xiaomin, Wu, Peilong, Wang, Haoyu
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 26.09.2025 Nature Publishing Group Nature Portfolio
Subjects:	3D indoor positioning 639/166/987 639/705/117 Accuracy Attention mechanism Data acquisition Datasets Denoising autoencoder Design Humanities and Social Sciences Indoor environments Integrated approach Localization multidisciplinary Neural networks Real time Science Science (multidisciplinary) Spatial discrimination Stacked variational autoencoder Wasserstein generative adversarial network Attention mechanism Denoising autoencoder 3D indoor positioning Stacked variational autoencoder Wasserstein generative adversarial network
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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