Methods for Enhancing Positioning Reliability in Indoor and Underground Satellite-shielded Environments

YI Qingwu; HUANG Lu; YU Baoguo; LIAO Guisheng

doi:10.11999/JEIT240870

Volume 47 Issue 5

May 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(5): 1529-1542

YI Qingwu, HUANG Lu, YU Baoguo, LIAO Guisheng. Methods for Enhancing Positioning Reliability in Indoor and Underground Satellite-shielded Environments[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1529-1542. doi: 10.11999/JEIT240870

Citation:

YI Qingwu, HUANG Lu, YU Baoguo, LIAO Guisheng. Methods for Enhancing Positioning Reliability in Indoor and Underground Satellite-shielded Environments[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1529-1542. doi: 10.11999/JEIT240870

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Methods for Enhancing Positioning Reliability in Indoor and Underground Satellite-shielded Environments

doi: 10.11999/JEIT240870 cstr: 32379.14.JEIT240870

YI Qingwu^{1, 2},
HUANG Lu^{2
,
,},
YU Baoguo²,
LIAO Guisheng¹

1.
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710077, China
2.
State Key Laboratory of comprehensive PNT Network and Equipment Technology, Shijiazhuang 050081, China

Funds: Key Technologies for Robust Centimeter-Level Ultra-Wideband Positioning in Urban Obstructed Environments (D2024523007), Research on Multi-Sensor Intelligent Fusion Positioning in Satellite-Denied Environments (F2024523004)

Received Date: 2024-10-15
Rev Recd Date: 2025-04-16

Available Online: 2025-05-06

Publish Date: 2025-05-01

Abstract

Abstract

This paper proposes a method to enhance the reliability of indoor positioning by combining an unsupervised autoencoder with nonlinear filtering. A Denoising Variational AutoEncoder model assisted by a deep Convolutional Neural Network (DVAE-CNN) is designed to regulate the positioning results from multiple aspects, including measurement data quality evaluation, target state transition modeling, and weight update strategies aided by environmental prior information. This approach addresses the issue of low positioning reliability caused by information loss, errors, and disturbances in complex indoor environments. Compared to positioning results without the reliability control mechanism, the proposed method improves average positioning accuracy by 74.6% and positioning reliability by 88.2%. Extensive experiments conducted in the venues of the Beijing 2022 Winter Olympics demonstrate that the proposed method provides highly robust, reliable, and continuous positioning services, showing significant potential for practical application and promotion. Objective With the rapid development of indoor positioning technologies, ensuring high reliability and trustworthiness in complex indoor and underground satellite-shielded environments remains a critical challenge. Existing methods often prioritize accuracy and continuity but neglect reliability under environmental disturbances such as signal loss, noise, and multipath effects. To address these limitations, this study proposes a multi-level trustworthiness enhancement framework by integrating an unsupervised Denoising Variational AutoEncoder with a Convolutional Neural Network (DVAE-CNN) and nonlinear particle filtering. The goal is to improve positioning reliability through data quality assessment, environmental prior information fusion, and adaptive state transition constraints, thereby supporting robust location-based services in challenging environments like the 2022 Beijing Winter Olympics venues. Methods The proposed framework combines a DVAE-CNN model for denoising and feature extraction with a particle filtering mechanism incorporating environmental priors and sensor data. The DVAE-CNN evaluates measurement data quality by reconstructing noisy inputs and identifying anomalies through reconstruction probability thresholds. Concurrently, nonlinear particle filtering integrates multi-source heterogeneous data (e.g., inertial sensors, Wi-Fi, and indoor maps) to constrain particle distributions based on motion patterns and structural boundaries. A weight update strategy dynamically adjusts particle importance using prior knowledge, while adaptive step-length estimation refines Pedestrian Dead Reckoning (PDR) to reduce cumulative errors. Results and Discussions Extensive experiments in controlled environments and real-world Olympic venues demonstrate significant improvements. Compared to baseline methods without trustworthiness mechanisms, the proposed approach achieves a 74.6% increase in average positioning accuracy and an 88.2% enhancement in reliability. In dynamic tests at the Beijing Winter Olympics venues, the method eliminated trajectory jumps caused by signal loss and improved coverage continuity by 34%, ensuring seamless navigation in complex indoor spaces. The fusion of DVAE-CNN-based anomaly detection and environmental constraints effectively suppressed “wall-penetrating” particles, enhancing result plausibility. Conclusions This study addresses the critical issue of positioning trustworthiness in indoor and underground environments by integrating data-driven anomaly detection with multi-source fusion. Key contributions include: (1) A DVAE-CNN model that improves data quality assessment and noise resilience; (2) A particle filtering framework leveraging environmental priors and adaptive PDR for robust state estimation; (3) Validation in high-stakes scenarios, achieving sub-meter accuracy and high reliability. Limitations, such as PDR’s cumulative errors, warrant further exploration. Future work will focus on real-time optimization and sensor noise modeling for broader applications.
- Indoor positioning,
- Trustworthy evaluation,
- Denoising variational autoencoder,
- Multi-source fusion,
- Particle filtering

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References(30)

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