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August 21, 2025Engineering Research Express

A multi-strategy optimized deep learning approach for INS/GNSS error compensation during GNSS Outages

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Authors

JLJianjuan LiuJDJuan DuZWZhuo Wang

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Overview

This approach improves GNSS positioning accuracy in vehicles during interruptions, indicating enhanced navigation reliability.

Key Points

  • The proposed method improves positioning accuracy by 72.85% when GNSS is interrupted for 50 seconds, highlighting its effectiveness.
  • In real-world tests, the method increases accuracy by 93.30% on straight sections and 75.83% on turns, emphasizing its practical benefits.
  • This approach utilizes a pre-trained deep learning model to predict velocity and position during losing GNSS signals, effectively addressing error accumulation.
  • The integration of LSTM, CNN, and optimization algorithms enhances the performance of the integrated INS/GNSS navigation system in complex environments.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68af56faad7bf08b1eadd31ahttps://doi.org/10.1088/2631-8695/adfe3e
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Also Consider

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

  1. 1An integrated navigation algorithm assisted by CNN-Informer during short-time GNSS outages2024 · 10 citations
  2. 2Error Compensation Method of GNSS/INS Integrated Navigation System Based on AT-LSTM During GNSS Outages2024 · 44 citations
  3. 3Deep Neural Network-enhanced Integrated Navigation Using Global Navigation Satellite System and Inertial Navigation System for Vehicle Safety Testing2026
  4. 4A long short term memory network-based, global navigation satellite system/inertial navigation system for unmanned surface vessels2024 · 8 citations
  5. 5A Hybrid GNSS/INS Integrated Navigation Method Combining GRU-SA and Covariance-Adaptive Factor Graph Optimization During GNSS Outages2025