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Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models.

He-Sheng Wang1, Dah-Jing Jwo1, Zhi-Hang Gao1

  • 1Department of Communications, Navigation, and Control Engineering, National Taiwan Ocean University, 2 Peining Road, Keelung 202301, Taiwan.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
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This summary is machine-generated.

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This study introduces an interpretable deep learning framework for Global Navigation Satellite System (GNSS) multipath detection. Layer-wise Relevance Propagation (LRP) reveals signal anomalies, enhancing navigation system reliability.

Area of Science:

  • Geomatics Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Global Navigation Satellite Systems (GNSS) increasingly use deep learning for signal processing.
  • Lack of interpretability in deep learning models poses risks for safety-critical GNSS applications.
  • Multipath effects are a significant challenge in GNSS signal analysis.

Purpose of the Study:

  • To develop an explainable deep learning framework for multipath effect detection in GNSS.
  • To enhance the interpretability of deep learning models used in GNSS applications.
  • To establish a novel method for anomaly detection in GNSS signals.

Main Methods:

  • Developed an interpretable Long Short-Term Memory (LSTM) architecture for GNSS observables.
  • Adapted Layer-wise Relevance Propagation (LRP) for GNSS signal analysis to attribute model decisions.
Keywords:
GNSSexplainabilitylayer-wise relevance propagationlong short-term memorymultipath

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  • Correlated LRP relevance scores with signal anomalies for anomaly detection.

    • Main Results:

    • The LSTM model achieved high prediction accuracy across GNSS parameters with maintained interpretability.
    • LRP relevance scores consistently increased during anomalous signal conditions (7.34%-32.48%).
    • Specific increases observed: multipath parameters (7.34-8.81%), carrier-to-noise ratios (12.50-32.48%), elevation angles (16.10%).

    Conclusions:

    • The LRP-based analysis enhances GNSS signal quality monitoring and integrity assessment.
    • The proposed approach provides a practical framework for detecting and analyzing GNSS signal anomalies.
    • This work contributes to developing more reliable and trustworthy navigation systems.