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Sensors 2017, 17(8), 1921;

An Extended Kalman Filter-Based Attitude Tracking Algorithm for Star Sensors

School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
Author to whom correspondence should be addressed.
Received: 15 June 2017 / Revised: 15 August 2017 / Accepted: 16 August 2017 / Published: 21 August 2017
(This article belongs to the Special Issue Inertial Sensors for Positioning and Navigation)
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Efficiency and reliability are key issues when a star sensor operates in tracking mode. In the case of high attitude dynamics, the performance of existing attitude tracking algorithms degenerates rapidly. In this paper an extended Kalman filtering-based attitude tracking algorithm is presented. The star sensor is modeled as a nonlinear stochastic system with the state estimate providing the three degree-of-freedom attitude quaternion and angular velocity. The star positions in the star image are predicted and measured to estimate the optimal attitude. Furthermore, all the cataloged stars observed in the sensor field-of-view according the predicted image motion are accessed using a catalog partition table to speed up the tracking, called star mapping. Software simulation and night-sky experiment are performed to validate the efficiency and reliability of the proposed method. View Full-Text
Keywords: star sensor; attitude tracking; extended Kalman filter star sensor; attitude tracking; extended Kalman filter

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Li, J.; Wei, X.; Zhang, G. An Extended Kalman Filter-Based Attitude Tracking Algorithm for Star Sensors. Sensors 2017, 17, 1921.

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