Decomposed Spacecraft Motion Estimation Algorithm Using GNSS Data

The article discusses the problem of estimating the spacecraft trajectory parameters based on data provided by GNSS (Global Navigation Satellite System) receiver and onboard spacecraft motion prediction algorithms. The traditional approach to solving the mentioned problem relies on Kalman filtering techniques for estimating the spacecraft’s coordinates and velocity. In this case the navigation algorithms in the onboard computer must meet the technical requirements for maximum reduction in computational load. The paper proposes a method of decomposition for onboard integration algorithm using three parallel second-order filters, which significantly reduces the computational load of the algorithm while maintaining the accuracy.

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Publication Details

Journal
Gyroscopy and Navigation
Published
2025-06-01
DOI
https://doi.org/10.1134/s207510872570018x
Primary Topic
Inertial Sensor and Navigation
Type
article
Field-Weighted Citation Impact
0.00
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article

Decomposed Spacecraft Motion Estimation Algorithm Using GNSS Data

Gyroscopy and Navigation
Inertial Sensor and Navigation
article

Decomposed Spacecraft Motion Estimation Algorithm Using GNSS Data

article en

Abstract

The article discusses the problem of estimating the spacecraft trajectory parameters based on data provided by GNSS (Global Navigation Satellite System) receiver and onboard spacecraft motion prediction algorithms. The traditional approach to solving the mentioned problem relies on Kalman filtering techniques for estimating the spacecraft’s coordinates and velocity. In this case the navigation algorithms in the onboard computer must meet the technical requirements for maximum reduction in computational load. The paper proposes a method of decomposition for onboard integration algorithm using three parallel second-order filters, which significantly reduces the computational load of the algorithm while maintaining the accuracy.

Gyroscopy and NavigationVol. 16(2)
Lomonosov Moscow State University (RU), All Russia Research Institute of Automatics (RU)
Openalex Percentile: Top 32%
Inertial Sensor and Navigation
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