Attitude determination of nanosatellites in the sun-eclipse phases

Hajiyev, Chingiz; Cilden–Guler, Demet; Somov, Yevgeny

doi:10.1109/rast.2017.8002990

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…This adaptive behavior of the SVD-aided EKF during the eclipse phase was found to be a significant advantage over the traditional EKF, which did not incorporate such an adaptation. As a result, the SVD-aided EKF demonstrated superior performance in terms of attitude estimation accuracy during this challenging phase of the satellite's orbit, as shown in the research findings by [22]. In contrast, the research revealed that the SVD-aided EKF may exhibit poor performance over a longer period (e.g., over 1000 s) compared to the traditional EKF, particularly during extended eclipse phases.…”

Section: Introductionmentioning

confidence: 74%

“…The EM algorithm approximates the joint likelihood function as its minimum variance estimate as shown in (22). To determine the minimum variance estimate, we compute the joint log likelihood log pσ k (x k , z 1:k ) and the second posterior PDF p σ k (x k |z k ).…”

Section: E Stepmentioning

confidence: 99%

“…To obtain the minimum variance estimate, Equations ( 28) and ( 30) are substituted into Equation (22). The resulting expression is…”

Section: E Stepmentioning

confidence: 99%

“…Using only magnetometer measurements, the proposed method can estimate rough attitude and angular rate even during eclipse phases; therefore, it can also be used in case of gyroscope failure. In [22], it was found that the SVD-aided EKF outperformed the traditional EKF during the eclipse phase. This improved performance was due to the capability of the SVD-aided EKF to adapt the measurement error covariance matrix (R) when the ADS lost sensor measurements.…”

Section: Introductionmentioning

confidence: 99%

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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: 74%

Section: E Stepmentioning

confidence: 99%

“…To obtain the minimum variance estimate, Equations ( 28) and ( 30) are substituted into Equation (22). The resulting expression is…”

Section: E Stepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations