Abstract
As an important error in star centroid location estimation, the systematic error greatly restricts the accuracy of the three-axis attitude supplied by a star sensor. In this paper, an analytical study about the behavior of the systematic error in the center of mass (CoM) centroid estimation method under different Gaussian widths of starlight energy distribution is presented by means of frequency field analysis and numerical simulations. Subsequently, an optimized extreme learning machine (ELM) based on the bat algorithm (BA) is adopted to predict the systematic error of the actual star centroid position and then compensate the systematic error from the CoM method. In the BA-ELM model, the input weights matrix and hidden layer biases parameters are encoded as microbat’s locations and optimized by utilizing the strong global search capacity of BA, which significantly improves the performance of ELM in terms of prediction accuracy. The simulation result indicates that our method can reduce the systematic error to less than 3.0 × 10−7 pixels, and its compensation accuracy is two or three orders of magnitude higher than that of other methods for estimating a star centroid location under a 3 × 3 pixel sampling window.
Highlights
Reliable and accurate attitude determination plays a significant role in aerospace missions.Star sensors provide the most accurate three-axis attitude information when compared with other attitude measurement devices such as the sun sensor, magnetometer, and gyroscope [1,2]
This paper presents an analysis of the systematic error in the center of mass (CoM) method under different Gaussian widths of starlight energy distribution utilizing a frequency field approach and numerical simulations, and it indicates that the systematic error consists of approximate error and truncation error
We propose a new compensation method based on the bat algorithm (BA)-extreme learning machine (ELM) model, in which the input weights matrix and hidden layer biases in ELM are optimized by utilizing the search ability of BA to remove adverse effects of random parameters, and the optimal results can be used to enhance the stability and prediction accuracy of the network with a lesser number of hidden neurons
Summary
Reliable and accurate attitude determination plays a significant role in aerospace missions. Star sensors provide the most accurate three-axis attitude information when compared with other attitude measurement devices such as the sun sensor, magnetometer, and gyroscope [1,2]. Star sensors are widely equipped on orbiting satellites and interplanetary spacecraft [3,4,5]. Attitude estimation by star sensors proceeds by comparing star locations in the image taken by star sensors with those in the predefined on-board catalogue. The accuracy of estimating star centroid locations is one critical factor which directly affects the performance of the star sensor [6]. We apply the extreme learning machine optimized by the bat algorithm (BA-ELM) to improve the star centroiding accuracy
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
