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Multi-Radio and/or Multi-Sensor Integrated Navigation System—2nd Edition
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Multi-Radio and/or Multi-Sensor Integrated Navigation System—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1099

Special Issue Editor

Dr. Dong-Hwan Hwang

E-Mail Website
Guest Editor
Department of Electronics Engineering, Chungnam National University, 99 Daehak-Ro, Yusong-Gu, Daejon 34134, Republic of Korea
Interests: GPS/INS; multi-radio integrated navigation system; GF-INS (gyro-free INS); military application of navigation system; GNSS application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global Navigation Satellite Systems (GNSSs) have become essential in positioning and navigation. However, GNSSs can be easily attacked by jamming, meaconing, and spoofing, since the GNSS signal strength is very weak and the received signal structure for civil use is open to the public. In addition, GNSS signals are not available inside buildings. GPS/INS integrated navigation systems are known to produce continuous navigation information, with various other multi-sensor integrated navigation systems being announced. In order to overcome signal attacks, many countries have plans to create alternative positioning, navigation, and timing (APNT) using local radio navigation systems such as distance measuring equipment (DME), enhanced long-range navigation (e-Loran), long-range navigation (Loran-C), and very high-frequency omnidirectional radio ranges (VORs). In order to obtain navigation information indoors, dead reckoning (DR), ultra-wide band (UWB) and wireless fidelity (Wi-Fi) have been integrated. This Special Issue aims to provide an overview of the latest research and development (R&D) regarding multi-radio and multi-sensor integrated navigation systems. The scope of this Special Issue includes, but is not limited to, the following topics.

Dr. Dong-Hwan Hwang
Guest Editor

Manuscript Submission Information

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Keywords

  • multi-radio integrated navigation systems
  • multi-sensor integrated navigation systems
  • alternative positioning, navigation, and timing
  • indoor navigation systems
  • space/aircraft navigation systems
  • marine navigation systems
  • land navigation systems
  • military applications
  • autonomous vehicle applications

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Published Papers (1 paper)

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Research

28 pages, 4405 KiB  
Article
Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
by He-Sheng Wang, Dah-Jing Jwo and Zhi-Hang Gao
Sensors 2025, 25(3), 978; https://doi.org/10.3390/s25030978 - 6 Feb 2025
Viewed by 890
Abstract
This paper addresses the critical challenge of understanding and interpreting deep learning models in Global Navigation Satellite System (GNSS) applications, specifically focusing on multipath effect detection and analysis. As GNSS systems become increasingly reliant on deep learning for signal processing, the lack of [...] Read more.
This paper addresses the critical challenge of understanding and interpreting deep learning models in Global Navigation Satellite System (GNSS) applications, specifically focusing on multipath effect detection and analysis. As GNSS systems become increasingly reliant on deep learning for signal processing, the lack of model interpretability poses significant risks for safety-critical applications. We propose a novel approach combining Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells with Layer-wise Relevance Propagation (LRP) to create an explainable framework for multipath detection. Our key contributions include: (1) the development of an interpretable LSTM architecture for processing GNSS observables, including multipath variables, carrier-to-noise ratios, and satellite elevation angles; (2) the adaptation of the LRP technique for GNSS signal analysis, enabling attribution of model decisions to specific input features; and (3) the discovery of a correlation between LRP relevance scores and signal anomalies, leading to a new method for anomaly detection. Through systematic experimental validation, we demonstrate that our LSTM model achieves high prediction accuracy across all GNSS parameters while maintaining interpretability. A significant finding emerges from our controlled experiments: LRP relevance scores consistently increase during anomalous signal conditions, with growth rates varying from 7.34% to 32.48% depending on the feature type. In our validation experiments, we systematically introduced signal anomalies in specific time segments of the data sequence and observed corresponding increases in LRP scores: multipath parameters showed increases of 7.34–8.81%, carrier-to-noise ratios exhibited changes of 12.50–32.48%, and elevation angle parameters increased by 16.10%. These results demonstrate the potential of LRP-based analysis for enhancing GNSS signal quality monitoring and integrity assessment. Our approach not only improves the interpretability of deep learning models in GNSS applications but also provides a practical framework for detecting and analyzing signal anomalies, contributing to the development of more reliable and trustworthy navigation systems. Full article
(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System—2nd Edition)
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