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

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.