A rigorous attitude estimation method for satellite attitude determination based on star sensor

The objective of the research is to undergo a performance comparison in terms of accuracy, convergence time, amount of memory, etc. between the satellite attitude determination and attitude estimation using non-linear filters. The fundamental approach towards it is to design an OBC (On Board Computer) that would help in achieving a controlled output for the chosen plant (MicroMAS1 satellite). The attitude determination algorithm is implemented through TRIAD algorithm, which takes sensor readings of body frame and inertial frame of reference. Then it is used to determine the rotation matrix (DCM) by converting the matrix form into vector form and again back to matrix form to determine the 3x3 matrix, which includes all the Euler angle equations to determine the pitch, roll, yaw characteristics of the system. The attitude estimation algorithms involves the use of nonlinear filters which provide an added advantage that energy can be transferred in a designed manner and extra degrees of freedom are available in filter design. The Unscented Kalman Filter (UKF) is preferred as it addresses the problem using a deterministic sampling approach. Moreover, the non-linear filters are used to remove the noise error and disturbance caused by engine. The design of satellite attitude models involves more of a mathematical approach that would be dealt with MATLAB and SIMULINK operations.

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Design of an Extended Interacting Multiple Models Adaptive Estimator for Attitude Determination of a Stereoimagery Satellite

International Journal of Aerospace Engineering ◽

10.1155/2011/890502 ◽

2011 ◽

Vol 2011 ◽

pp. 1-19 ◽

Cited By ~ 2

Author(s):

Hossein Bolandi ◽

Farhad Fani Saberi ◽

Amir Mehrjardi Eslami

Keyword(s):

Attitude Determination ◽

Estimation Error ◽

Attitude Estimation ◽

Estimation Accuracy ◽

Multiple Models ◽

Uniform Motion ◽

Star Sensor ◽

Adaptive Estimator ◽

Motion Mode

We will design an extended interacting multiple models adaptive estimator (EIMMAE) for attitude determination of a stereoimagery satellite. This algorithm is based on interacting multiple models (IMM) extended kalman filters (EKF) using star sensor and gyroscope data. In this method, the motion of satellite during stereoimaging manoeuvres is partitioned into two different modes: “manoeuvring motion” mode and “uniform motion” mode. The proposed algorithm will select the suitable Kalman filter structure to estimate gyro errors accurately in order to maintain the peak attitude estimation error less enough at the beginning of manoeuvres while the satellite is in “manoeuvring motion” mode and then will select the suitable star sensor measurement noise level at the end of manoeuvres while the satellite is in “uniform motion” mode to reduce attitude estimation errors. It will be shown that using the proposed algorithm, the attitude estimation accuracy of stereoimagery satellite will be increased considerably. The effectiveness of the proposed algorithm will be examined and compared with the previous proposed methods through numerical simulations.

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A Markley Variables-based Attitude Estimation Method Using Optical Flow and a Star Vector for Spinning Spacecraft

Journal of Navigation ◽

10.1017/s0373463318000516 ◽

2018 ◽

Vol 71 (6) ◽

pp. 1589-1598 ◽

Cited By ~ 1

Author(s):

Xiaolin Ning ◽

Zonghe Ding ◽

Mingzhu Xu ◽

Jiancheng Fang ◽

Gang Liu

Keyword(s):

Optical Flow ◽

Angular Rate ◽

Estimation Method ◽

Attitude Estimation ◽

Spin Axis ◽

Estimation Methods ◽

Star Sensor ◽

Axis Orientation ◽

Sun Sensor ◽

The Impact

Markley variables have advantages of slow variation, easy numerical integration and high precision in describing the attitude of spinning spacecraft. Previous attitude estimation methods based on Markley variables for spinning spacecraft usually employ a sun vector from the sun sensor, a magnetic vector from the magnetometer, or the angular rate from the gyro as the measurement. This paper proposes a Markley variables-based attitude estimation method using optical flow and a star vector from a star sensor as the measurement, where optical flow provides rate information and the star vector provides direction information. This method can estimate the direction of the spin axis and spin angular rate very well by using only one star sensor. In addition, the star sensor has higher accuracy than the traditional sun sensor and magnetometer, and the star sensor can also replace the gyro in case the gyro is out of action. The impact factors of this method are also analysed, which include spin angular rate, spin axis orientation and spacecraft moment of inertia tensor error.

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HIGH-PRECISION ATTITUDE ESTIMATION METHOD OF STAR SENSORS AND GYRO BASED ON COMPLEMENTARY FILTER AND UNSCENTED KALMAN FILTER

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w1-49-2017 ◽

2017 ◽

Vol XLII-3/W1 ◽

pp. 49-53 ◽

Cited By ~ 4

Author(s):

C. Guo ◽

X. Tong ◽

S. Liu ◽

X. Lu ◽

...

Keyword(s):

Kalman Filter ◽

High Frequency ◽

Attitude Determination ◽

Unscented Kalman Filter ◽

Estimation Method ◽

Attitude Estimation ◽

Star Tracker ◽

Attitude Motion ◽

Complementary Filter ◽

Gyro Drift

Determining the attitude of satellite at the time of imaging then establishing the mathematical relationship between image points and ground points is essential in high-resolution remote sensing image mapping. Star tracker is insensitive to the high frequency attitude variation due to the measure noise and satellite jitter, but the low frequency attitude motion can be determined with high accuracy. Gyro, as a short-term reference to the satellite’s attitude, is sensitive to high frequency attitude change, but due to the existence of gyro drift and integral error, the attitude determination error increases with time. Based on the opposite noise frequency characteristics of two kinds of attitude sensors, this paper proposes an on-orbit attitude estimation method of star sensors and gyro based on Complementary Filter (CF) and Unscented Kalman Filter (UKF). In this study, the principle and implementation of the proposed method are described. First, gyro attitude quaternions are acquired based on the attitude kinematics equation. An attitude information fusion method is then introduced, which applies high-pass filtering and low-pass filtering to the gyro and star tracker, respectively. Second, the attitude fusion data based on CF are introduced as the observed values of UKF system in the process of measurement updating. The accuracy and effectiveness of the method are validated based on the simulated sensors attitude data. The obtained results indicate that the proposed method can suppress the gyro drift and measure noise of attitude sensors, improving the accuracy of the attitude determination significantly, comparing with the simulated on-orbit attitude and the attitude estimation results of the UKF defined by the same simulation parameters.

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