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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Summary: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, and Limited capability to capture global spatial features, which restrict their applicability in real-world scenarios. Additionally, the collection of indoor positioning data requires substantial human effort, resulting in high data acquisition costs. Consequently, generating high-quality, high-density 3D positioning data from a Limited number of real samples has become a critical issue. To address these Limitations, this paper proposes a novel 3D indoor positioning method that integrates deep spatial feature enhancement and attention-based denoising. Specifically, a stacked variational autoencoder (SVAE) is used to extract structured deep spatial representations, while a Wasserstein generative adversarial network (WGAN) synthesizes realistic high-density samples to mitigate data sparsity. An attention mechanism is embedded in the encoder to improve global feature perception and spatial awareness, and controlled noise injection during training enhances robustness against noisy measurements. Experimental results show that, with only 10% of the UJIIndoorLoc dataset generated, the proposed method combined with a simple deep neural network (DNN) achieves 100% building localization accuracy, 94.7% floor localization accuracy, and reduces positioning error by 14.32%. Similar improvements are observed on the Tampere and UTSIndoorLoc datasets, with floor localization accuracies of 92.83% and 94.33% and positioning error reductions of 15.18% and 18.89%, respectively. These results demonstrate the effectiveness of the method in enhancing 3D indoor positioning with limited data.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-18549-y