Abstract's details
Extended Kalman Filtering for DORIS : Modelling Choices and Performance Impacts
Event: 2026 IDS Workshop
Session: Advances in DORIS: Data, Antennas, and Modeling
Presentation type: Poster
Over the last few years, DSO has been developing its own in-house DORIS
data processing software tools, with the aim of delivering
state-of-the-art, free and open-source software to the DORIS community
through an open and collaborative development model. The software
architecture is built around the principles of efficiency, modularity,
and reusability, supporting both precise orbit determination and
positioning applications. A key distinction from software traditionally
used by DORIS Analysis Centers is the adoption of an extended Kalman
filter as the main framework for estimating the parameters of interest.
The present contribution focuses on the implementation of the filter and the
overall estimation strategy adopted by the software, with application to
DORIS data from recent missions over selected
time periods. For precise applications, several aspects require careful
treatment, including filter tuning, process noise definition, the choice
of estimated parameters and their stochastic properties, and the modeling
of the observation equations. These elements strongly influence the
stability and performance of the estimation process, and their proper
configuration is essential for achieving reliable results. Particular
attention is given to the balance between model complexity and numerical
robustness, especially in the context of operational processing.
A set of representative configurations is analysed, including different
process noise models and parameterizations, and their impact is evaluated
through orbit metrics. Particular attention is given to the balance between model complexity and numerical robustness, especially in the context of operational processing. Α range of modeling choices and their implications for performance, robustness, and estimation quality is discussed , highlighting the practical considerations that arise in the development of a modern DORIS processing system.
Back to the list of abstractdata processing software tools, with the aim of delivering
state-of-the-art, free and open-source software to the DORIS community
through an open and collaborative development model. The software
architecture is built around the principles of efficiency, modularity,
and reusability, supporting both precise orbit determination and
positioning applications. A key distinction from software traditionally
used by DORIS Analysis Centers is the adoption of an extended Kalman
filter as the main framework for estimating the parameters of interest.
The present contribution focuses on the implementation of the filter and the
overall estimation strategy adopted by the software, with application to
DORIS data from recent missions over selected
time periods. For precise applications, several aspects require careful
treatment, including filter tuning, process noise definition, the choice
of estimated parameters and their stochastic properties, and the modeling
of the observation equations. These elements strongly influence the
stability and performance of the estimation process, and their proper
configuration is essential for achieving reliable results. Particular
attention is given to the balance between model complexity and numerical
robustness, especially in the context of operational processing.
A set of representative configurations is analysed, including different
process noise models and parameterizations, and their impact is evaluated
through orbit metrics. Particular attention is given to the balance between model complexity and numerical robustness, especially in the context of operational processing. Α range of modeling choices and their implications for performance, robustness, and estimation quality is discussed , highlighting the practical considerations that arise in the development of a modern DORIS processing system.