29 September 2017 Development of a computationally efficient algorithm for attitude estimation of a remote sensing satellite
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Abstract
This paper presents a computationally efficient algorithm for attitude estimation of remote a sensing satellite. In this study, gyro, magnetometer, sun sensor and star tracker are used in Extended Kalman Filter (EKF) structure for the purpose of Attitude Determination (AD). However, utilizing all of the measurement data simultaneously in EKF structure increases computational burden. Specifically, assuming n observation vectors, an inverse of a 3n×3n matrix is required for gain calculation. In order to solve this problem, an efficient version of EKF, namely Murrell’s version, is employed. This method utilizes measurements separately at each sampling time for gain computation. Therefore, an inverse of a 3n×3n matrix is replaced by an inverse of a 3×3 matrix for each measurement vector. Moreover, gyro drifts during the time can reduce the pointing accuracy. Therefore, a calibration algorithm is utilized for estimation of the main gyro parameters.
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Amir Labibian, Amir Labibian, Amir Hossein Bahrami, Amir Hossein Bahrami, Javad Haghshenas, Javad Haghshenas, } "Development of a computationally efficient algorithm for attitude estimation of a remote sensing satellite", Proc. SPIE 10423, Sensors, Systems, and Next-Generation Satellites XXI, 1042326 (29 September 2017); doi: 10.1117/12.2280005; https://doi.org/10.1117/12.2280005
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