HE<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si243.svg" display="inline" id="d1e1932"><mml:msup><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>LM-AD: Hierarchical and efficient attitude determination framework with adaptive error compensation module based on ELM network

Cao, Kailang; Li, Jiaojiao; Song, Rui; Li, Yunsong

doi:10.1016/j.isprsjprs.2022.12.010

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…The joint log-likelihood function can be computed using ( 23)- (25). The logarithm of the normal distribution…”

Section: E Stepmentioning

confidence: 99%

“…When the process noise increases or the spacecraft is in eclipse, SVD-aided adaptive EKF performs better than SVD-aided EKF. The authors in [25] propose a hierarchical and efficient framework for satellite attitude determination that aims to compensate for observation errors in raw attitude data. It includes a simplified adaptive Kalman filtering module, a neural-networkbased system error compensation module, and a weighted attitude smoothing module.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…The joint log-likelihood function can be computed using ( 23)- (25). The logarithm of the normal distribution…”

Section: E Stepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%