What question did this study set out to answer?

The research aims to enhance the robustness of GNSS timescales using a deep learning approach for composite clocks.

February 9, 20260 citationsOpen Access

Deep Learning Assisted Composite Clock: Robust Timescale for GNSS Through Neural Network

GFGaetan Fayon AMA. Mudrak HSHugo Sobreira

Key Points

The research aims to enhance the robustness of GNSS timescales using a deep learning approach for composite clocks.
Developed a Deep Learning Assisted Composite Clock (DLACC) using a neural network approach.
Compared the performance of DLACC against traditional Kalman filter-based systems.
Evaluated the neural network's capability in managing ensemble clock reconfigurations.
DLACC demonstrated better performance compared to conventional Kalman filters.
The neural network improved modeling of clock dynamics under non-linear conditions.
The findings indicate a more resilient and adaptive GNSS timescale using DLACC.

Abstract

This study introduces the Deep Learning Assisted Composite Clock (DLACC), aiming to improve the robustness of the GNSS timescale. If traditional Kalman filter-based composite clocks are today used in systems like GPS and EGNOS, the non-linear, non-Gaussian, and non-stationary behavior of atomic clocks can impact the performance of such model-based filtering. DLACC, built from the KalmanNet approach, proposes to enhance Kalman filters by computing its gain through a neural network to better model clock dynamics and manage ensemble clock reconfigurations. In particular, this study evaluates this method’s performance against conventional filters, demonstrating its potential for more resilient and adaptive GNSS timescales.

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