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October 3, 2025Engineering Research Express

Inertial Sensor Measurement Calibration Using Artificial Intelligence Techniques: A Comparative Study

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Authors

MMMohammed Majid MsallamFAFarah Flayyeh AlkhalidSJSeerwan Waleed Jirjees

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Overview

This comparative study reveals that neural networks improve inertial navigation systems' accuracy during GPS outages.

Key Points

  • Neural networks enhance predictions of inertial navigation system errors during GPS signal loss, improving navigation reliability.
  • The study demonstrates that RNN models achieve a standard deviation of 0.0864 m for position accuracy, indicating advanced error correction.
  • Artificial intelligence techniques are applied to denoise measurements and predict errors in inertial navigation systems effectively.
  • The findings highlight the potential of combining GPS and INS for more reliable vehicle navigation in challenging environments.

Cite This Study

Msallam et al. (2025) studied this question.

synapsesocial.com/papers/68e034f7f0e39f13e7fa305ehttps://doi.org/10.1088/2631-8695/ae0f0a
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Deep Neural Network-enhanced Integrated Navigation Using Global Navigation Satellite System and Inertial Navigation System for Vehicle Safety Testing2026
  2. 2Self-Calibration of INS/Odometer Integrated Navigation System With a Large Initial Position Error2024 · 1 citations
  3. 3Enhancing Pure Inertial Navigation Accuracy through a Redundant High-Precision Accelerometer-Based Method Utilizing Neural Networks2024 · 11 citations
  4. 4A multi-strategy optimized deep learning approach for INS/GNSS error compensation during GNSS Outages2025 · 1 citations
  5. 5Artificial neural network based on strong track and square root UKF for INS/GNSS intelligence integrated system during GPS outage2024 · 14 citations