Accurate Direct Strapdown Direction Cosine Algorithm

Zhang, Xu; Xie, Jing; Zhou, Zebing; Zhao, Junyang; Xu, Zhiqiang

doi:10.1109/taes.2018.2881353

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…The random direction cosine matrices (DCM) with shape

served as the ground-truth attitude matrices [ 33 ]. The reference vectors were simply rotated by each attitude matrix to generate the body vectors

with a shape

as follows:

where

is a vector of measurement errors drawn from a zero-mean Gaussian distribution

with standard deviations

serving as additive white Gaussian noise (AWGN) [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Static Attitude Determination Using Convolutional Neural Networks

Santos

Seman

Bezerra

et al. 2021

Sensors

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The need to estimate the orientation between frames of reference is crucial in spacecraft navigation. Robust algorithms for this type of problem have been built by following algebraic approaches, but data-driven solutions are becoming more appealing due to their stochastic nature. Hence, an approach based on convolutional neural networks in order to deal with measurement uncertainty in static attitude determination problems is proposed in this paper. PointNet models were trained with different datasets containing different numbers of observation vectors that were used to build attitude profile matrices, which were the inputs of the system. The uncertainty of measurements in the test scenarios was taken into consideration when choosing the best model. The proposed model, which used convolutional neural networks, proved to be less sensitive to higher noise than traditional algorithms, such as singular value decomposition (SVD), the q-method, the quaternion estimator (QUEST), and the second estimator of the optimal quaternion (ESOQ2).

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“…The random direction cosine matrices (DCM) with shape

served as the ground-truth attitude matrices [ 33 ]. The reference vectors were simply rotated by each attitude matrix to generate the body vectors

with a shape

as follows:

where

is a vector of measurement errors drawn from a zero-mean Gaussian distribution

with standard deviations

serving as additive white Gaussian noise (AWGN) [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

“…The random direction cosine matrices (DCM) with shape (S, 3, 3) served as the groundtruth attitude matrices [33]. The reference vectors were simply rotated by each attitude matrix to generate the body vectors #» b with a shape (S, O, 3) as follows:…”

Section: Datasetmentioning

confidence: 99%

Static Attitude Determination Using Convolutional Neural Networks

Santos

Seman

Bezerra

et al. 2021

Sensors

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SINS attitude algorithm based on moving-window overdetermined polynomial fitting of gyro outputs

Fang¹,

Chang²,

Bao³

et al. 2021

Meas. Sci. Technol.

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The attitude algorithm is the most important part of the whole strapdown inertial navigation processing. It calculates the attitude of certain parameterization by integrating the gyro outputs or measurements in a specifically tailored way according to the attitude kinematic differential equation. The measurements or some angular velocity models obtained by fitting these measurements are often assumed free of errors in order to assess the numerical errors only. However, the gyro outputs and hence the models from them are by no means free of measurement errors. It is more often than not that the measurement errors dominate the numerical ones in practice. In this study, with coping with the measurement errors as the focus, we aim to improve the angular velocity model which is used as input in an attitude integration algorithm. This is achieved by exploiting the potential of overdetermined least-squares polynomial fitting. In order to avoid reducing the update rate by incorporating more measurements, the moving window trick is employed to re-use measurements in the previous update interval. The conventional attitude algorithm with second-order approximation in solving the differential equation of the equivalent rotation vector is employed as an example; however, the proposed method can be readily applied to other parameterizations such as direction cosine matrix, quaternion or Rodrigues parameters, and other high order approximations in solving the differential equation widely studied recently.

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Attitude Reconstruction From Inertial Measurements: QuatFIter and Its Comparison with RodFIter

Yan

2019

IEEE Trans. Aerosp. Electron. Syst.

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Accurate Direct Strapdown Direction Cosine Algorithm

Cited by 10 publications

References 21 publications

Static Attitude Determination Using Convolutional Neural Networks

Static Attitude Determination Using Convolutional Neural Networks

SINS attitude algorithm based on moving-window overdetermined polynomial fitting of gyro outputs

Attitude Reconstruction From Inertial Measurements: QuatFIter and Its Comparison with RodFIter

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