Gyroless Nanosatellite Attitude Estimation in Loss of Sun Sensor Measurements

Hajiyev, Chingiz; Cilden–Guler, Demet

doi:10.1016/j.ifacol.2018.11.254

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…As a result, the SVD-aided EKF may not be well suited for attitude estimation during such prolonged eclipse phases. Another paper [23] proposes a prediction approach for estimating satellite attitude during the eclipse phase and an adaptive scheme that utilizes the SVD-aided EKF to determine the sun phase. However, the study identified that the accuracy of the prediction algorithm was compromised due to the prolonged duration of the eclipse period.…”

Section: Introductionmentioning

confidence: 99%

Attitude Determination System for a Cubesat Experiencing Eclipse

Mmopelwa,

Ramodimo,

Matsebe

et al. 2023

Sensors

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In the context of Kalman filters, the predicted error covariance matrix Pk+1 and measurement noise covariance matrix R are used to represent the uncertainty of state variables and measurement noise, respectively. However, in real-world situations, these matrices may vary with time due to measurement faults. To address this issue in CubeSat attitude estimation, an adaptive extended Kalman filter has been proposed that can dynamically estimate the predicted error covariance matrix and measurement noise covariance matrix using an expectation-maximization approach. Simulation experiments have shown that this algorithm outperforms existing methods in terms of attitude estimation accuracy, particularly in sunlit and shadowed phases of the orbit, with the same filtering parameters and initial conditions.

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Section: Introductionmentioning

confidence: 99%

Attitude Determination System for a Cubesat Experiencing Eclipse

Mmopelwa,

Ramodimo,

Matsebe

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The satellite attitude determination of a Low Earth Orbit (LEO) has many applications, especially in many scientific missions, for instance astronomic and astrophysical ones [1–3]. In the case of data available from sensors, such as sun sensor, star tracker, gyroscope, etc.…”

Section: Introductionmentioning

confidence: 99%