Improved Gaussian process regression-based method to bridge GPS outages in INS/GPS integrated navigation systems

Zhu, Yixian; Zhang, Minmin; Yang, Yanan; Ran, Changyan; Zhou, Ling

doi:10.1016/j.measurement.2024.114432

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Cited by 4 publications

References 36 publications

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Schuler period oscillation error suppression for inertial navigation systems based on reverse navigation and wavelet transforms

Peng,

Wu,

Yao

et al. 2025

Measurement

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Schuler period oscillation error suppression for inertial navigation systems based on reverse navigation and wavelet transforms

Peng,

Wu,

Yao

et al. 2025

Measurement

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Media Bus: XR-Based Immersive Cultural Heritage Tourism

Du,

Lee,

Lee

et al. 2024

SIGGRAPH Asia 2024 Posters

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A Novel FECAM-iTransformer Algorithm for Assisting INS/GNSS Navigation System during GNSS Outages

Kuang,

Yan

2024

Applied Sciences

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In the field of navigation and positioning, the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system is known for providing stable and high-precision navigation services for vehicles. However, in extreme scenarios where GNSS navigation data are completely interrupted, the positioning accuracy of these integrated systems declines sharply. While there has been considerable research into using neural networks to replace the GNSS signal output during such interruptions, these approaches often lack targeted modeling of sensor information, resulting in poor navigation stability. In this study, we propose an integrated navigation system assisted by a novel neural network: an inverted-Transformer (iTransformer) and the application of a frequency-enhanced channel attention mechanism (FECAM) to enhance its performance, called an INS/FECAM-iTransformer integrated navigation system. The key advantage of this system lies in its ability to simultaneously extract features from both the time and frequency domains and capture the variable correlations among multi-channel measurements, thereby enhancing the modeling capabilities for sensor data. In the experimental part, a public dataset and a private dataset are used for testing. The best experimental results show that compared to a pure INS inertial navigation system, the position error of the INS/FECAM-iTransformer integrated navigation system reduces by up to 99.9%. Compared to the INS/LSTM (long short-term memory) and INS/GRU (gated recurrent unit) integrated navigation systems, the position error of the proposed method decreases by up to 82.4% and 78.2%, respectively. The proposed approach offers significantly higher navigation accuracy and stability.

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Improved Gaussian process regression-based method to bridge GPS outages in INS/GPS integrated navigation systems

Cited by 4 publications

References 36 publications

Schuler period oscillation error suppression for inertial navigation systems based on reverse navigation and wavelet transforms

Schuler period oscillation error suppression for inertial navigation systems based on reverse navigation and wavelet transforms

Media Bus: XR-Based Immersive Cultural Heritage Tourism

A Novel FECAM-iTransformer Algorithm for Assisting INS/GNSS Navigation System during GNSS Outages

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