Mortar Trajectory Estimation by a Deep Error-State Kalman Filter in a GNSS-Denied Environment

Roux, Alicia; Changey, Sébastien; Lauffenburger, Jean-Philippe; Weber, Jonathan

doi:10.1109/plans53410.2023.10140013

Cited by 1 publication

(1 citation statement)

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“…A Kalman filter is a Bayesian-based minimum mean-square estimation method, which lies in adding state constraints to the observation equations, thereby constructing reliable function models and stochastic models [18][19][20]. However, it is difficult to handle state equations and system errors in complex environments.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Xie,

Wang,

et al. 2024

Applied Sciences

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Hydraulic structures are the key national infrastructures, whose safety and stability are crucial for socio-economic development. Global Navigation Satellite System (GNSS) technology, as a high-precision deformation monitoring method, is of great significance for the safety and stability of hydraulic structures. However, the GNSS time series exhibits characteristics such as high nonlinearity, spatiotemporal correlation, and noise interference, making it difficult to model for prediction. The Neural Networks (CNN) model has strong feature extraction capabilities and translation invariance. However, it remains sensitive to changes in the scale and position of the target and requires large amounts of data. The Gated Recurrent Units (GRU) model could improve the training effectiveness by introducing gate mechanisms, but its ability to model long-term dependencies is limited. This study proposes a combined model, using CNN to extract spatial features and GRU to capture temporal information, to achieve an accurate prediction. The experiment shows that the proposed CNN-GRU model has a better performance, with an improvement of approximately 45%, demonstrating higher accuracy and reliability in predictions for GNSS deformation monitoring. This provides a new feasible solution for the safety monitoring and early warning of hydraulic structures.

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Section: Introductionmentioning

confidence: 99%