Sensor Alignment Calibration for PrecisionAttitude Determination of Spacecrafts

Lee, Il-Hyoung; Ryoo, Chang-Kyung; Bang, Hyochoong; Tahk, Min-Jea; Lee, Sang-Ryool

doi:10.5139/ijass.2004.5.1.083

Cited by 10 publications

(2 citation statements)

References 3 publications

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“…What is more, information on one or two axes may be insoluble with an improper route. The Kalman filter is the most widely used system-level method currently in both laboratory and online calibration [10][11][12][13]. However, initial guesses according with system parameters are indispensable and calibration results will be seriously influenced by these values.…”

Section: Introductionmentioning

confidence: 99%

Star sensor installation error calibration in stellar-inertial navigation system with a regularized backpropagation neural network

Zhang

Niu

et al. 2018

Meas. Sci. Technol.

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The star sensor is the attitude reference in a stellar-inertial navigation system. It is essential to acquire the star sensor installation error, which has a great influence on the system navigation performance. However, traditional methods have a poor tolerance for a large range of installation errors, especially when the system works under a separate installation mode. In this paper a novel calibration method, using a regularized backpropagation (BP) neural network, is proposed. With a specially designed calibration procedure, the neural network is structured with BP and the regularization is improved. The network training is conducted for parameter solidification. The calibration can be achieved without formula derivation and numerical calculation under both small and large installation errors. In the experiment, the calibration accuracy is about 5 arcsec under small installation errors and about 20 arcsec under large installation errors, which is much better than a Kalman filter. The proposed method has the potential to be a universal star sensor calibration method under integrative installation mode or separated installation mode with large installation error.

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Section: Introductionmentioning

confidence: 99%

Star sensor installation error calibration in stellar-inertial navigation system with a regularized backpropagation neural network

Zhang

Niu

et al. 2018

Meas. Sci. Technol.

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“…It has been proved that lens distortions have a great effect on the large field of view star sensors and the effect cannot be ignored with the development of star sensors [ 16 ]. In conclusion, establishing a comprehensive error model including the principal point drift , the focal length error, the distortions as well as the image plane tilt and rotary errors, is important for high-precision star sensors [ 17 , 18 ]. The effect of the image plane rotary-tilt errors and the distortions on measurement accuracy of star sensors is worthy of further investigation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Error Model of Optical Systems and an On-Orbit Calibration Method for Star Sensors

Wang

Geng

Jin

2015

Sensors

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In order to improve the on-orbit measurement accuracy of star sensors, the effects of image-plane rotary error, image-plane tilt error and distortions of optical systems resulting from the on-orbit thermal environment were studied in this paper. Since these issues will affect the precision of star image point positions, in this paper, a novel measurement error model based on the traditional error model is explored. Due to the orthonormal characteristics of image-plane rotary-tilt errors and the strong nonlinearity among these error parameters, it is difficult to calibrate all the parameters simultaneously. To solve this difficulty, for the new error model, a modified two-step calibration method based on the Extended Kalman Filter (EKF) and Least Square Methods (LSM) is presented. The former one is used to calibrate the main point drift, focal length error and distortions of optical systems while the latter estimates the image-plane rotary-tilt errors. With this calibration method, the precision of star image point position influenced by the above errors is greatly improved from 15.42% to 1.389%. Finally, the simulation results demonstrate that the presented measurement error model for star sensors has higher precision. Moreover, the proposed two-step method can effectively calibrate model error parameters, and the calibration precision of on-orbit star sensors is also improved obviously.

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