Fundamentals of Spacecraft Attitude Determination and Control

Markley, F. Landis; Crassidis, John L.

doi:10.1007/978-1-4939-0802-8

Cited by 720 publications

(553 citation statements)

References 4 publications

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“…Extracting robust feature points with fast computation is a fundamental problem in image processing, and many feature extraction algorithms have been proposed (e.g., [10,23]). In this study, feature points are identified by the SURF feature descriptor, which robustly extracts features from different views of the same scene.…”

Section: Feature Descriptormentioning

confidence: 99%

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Satellite Attitude Determination and Map Projection Based on Robust Image Matching

et al. 2017

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Small satellites have limited payload and their attitudes are sometimes difficult to determine from the limited onboard sensors alone. Wrong attitudes lead to inaccurate map projections and measurements that require post-processing correction. In this study, we propose an automated and robust scheme that derives the satellite attitude from its observation images and known satellite position by matching land features from an observed image and from well-registered base-map images. The scheme combines computer vision algorithms (i.e., feature detection, and robust optimization) and geometrical constraints of the satellite observation. Applying the proposed method to UNIFORM-1 observations, which is a 50 kg class small satellite, satellite attitudes were determined with an accuracy of 0.02 • , comparable to that of star trackers, if the satellite position is accurately determined. Map-projected images can be generated based on the accurate attitudes. Errors in the satellite position can add systematic errors to derived attitudes. The proposed scheme focuses on determining satellite attitude with feature detection algorithms applying to raw satellite images, unlike image registration studies which register already map-projected images. By delivering accurate attitude determination and map projection, the proposed method can improve the image geometries of small satellites, and thus reveal fine-scale information about the Earth.

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Section: Feature Descriptormentioning

confidence: 99%

“…The commonly available methods for satellite determinations are STTs, Sun sensors, gyroscopes, geomagnetic sensors, and horizon sensors [10]. STTs and horizon sensors are based on image processing, but they do not utilize land features.…”

Section: Introductionmentioning

confidence: 99%

Satellite Attitude Determination and Map Projection Based on Robust Image Matching

et al. 2017

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“…The variations in the Earth's geomagnetic field are related to the dynamo within the Earth's core, and crust which is taken into consideration in this study, see (3). Here, IGRF (International Geomagnetic Reference Field) is used for the magnetic field model (in nano Tesla), defining 4-inputvariable (r, u, f, t), using numerical Gauss coefficients (g, h) -the global variables in the IGRF algorithm [16].…”

Section: Measurement Models 321 Earth's Magnetic Field Directionmentioning

confidence: 99%

“…Single-frame method is one of the common methods used for small satellite attitude determination system. In this method, vectors coming from the selected sensor data and developed models can be placed in Wahba's problem [1][2][3]. Coordinate systems used as reference frame and body frame can be transformed to each other with necessary input parameters.…”

Section: Introductionmentioning

confidence: 99%

Singular value decomposition based satellite attitude determination using different sensor configurations

Cilden–Guler

Hajiyev

2017

Int. J. Metrol. Qual. Eng.

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Abstract. Satellite's attitude can be determined by using several different ways. In order to use a recursive filter to determine the attitude especially after crucial intervals for the satellite including detumbling, eclipse, etc., single-frame methods can be used, and singular value decomposition (SVD) method has been selected because of its robustness for this paper. They can be placed for the initial values, or continuous input to the filter in addition to covariance matrix at each step. Also, sensor configurations depending on the position of the Sun are considered and used in the SVD method. Wahba's loss function is minimized with separating the basic matrix including the reference and model vectors into singular values because many researches show the SVD as the most robust algorithm after error analyses between several single-frame methods. Sun sensor, magnetometer, and horizon sensor are the selected common attitude sensors for small satellites in order to compare their attitude determination performances. For this purpose, absolute errors for different sensor configurations in the simulation environment in addition to the RMS errors are presented. The purpose of the paper is to present an attitude determination concept for a nanosatellite. Also, different sensor configurations are considered in the single-frame method with switching logic depending on the vector data. Horizon sensor and sun sensor are the optimum pair for the light side of the Earth on the other hand, during the eclipse; horizon sensor and magnetometer give the most accurate results. Study shows that the proposed algorithm can be performed for initial values to the Kalman filter, and in the transient phase before the filter tuned in practical satellite usage. In the eclipse period, sun-independent sensors are used and changing configurations provide better attitude estimation results to filtering techniques.

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“…In MEKF algorithm, attitude quaternion is represented by the product of estimated attitude quaternion and a deviation from that estimate. Using the MEKF algorithm can solve the quaternion normalization constraints problems [4,5]. The main idea of the MEKF algorithm is use the EKF algorithm to estimate the vector part of quaternion, then use the corrected quaternion to represents the satellite attitude.…”

Section: Introductionmentioning

confidence: 99%

The Application of Improved Extended Kalman Filter Algorithm in Satellite Attitude Determination

Jiang¹,

Xin-pu²,

Wu³

et al. 2016

Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications

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Abstract. The classical MEKF algorithm represent the estimation error vector in the estimated attitude coordinates, without considering the deviation between the estimated attitude coordinate and the true attitude coordinate. GEKF algorithm used geometric transform introduced error consistent representation method, which solve the error vector representation inconsistency problem caused by the deviation. But the GEKF algorithm only consider the gyro's constant drift, without considering the time related drift of gyro. To solve this problem, this paper proposes an IGEKF algorithm, the algorithm extended state variable dimensions, using the IGEKF algorithm to estimate the gyro constant drift and time related drift. Simulation results show that the IGEKF algorithm for estimation of gyro drift is more accurate than the GEKF algorithm, the filtering precision has improved more than GEKF algorithm.

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Fundamentals of Spacecraft Attitude Determination and Control

Cited by 720 publications

References 4 publications

Satellite Attitude Determination and Map Projection Based on Robust Image Matching

Satellite Attitude Determination and Map Projection Based on Robust Image Matching

Singular value decomposition based satellite attitude determination using different sensor configurations

The Application of Improved Extended Kalman Filter Algorithm in Satellite Attitude Determination

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