A Systematic Error Compensation Method Based on an Optimized Extreme Learning Machine for Star Sensor Image Centroid Estimation

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.

MVDT-SI: A Multi-View Double-Triangle Algorithm for Star Identification

“…The first step-centroid estimation-has been well studied. For example, the star centroid location estimation algorithm proposed by Wei [5] reduces the systematic error to less than 3.0 × 10 −7 pixels. Therefore, this paper focuses on the second step-star identification, which is still a challenge.…”

“…The main function of the star sensor includes: star centroid estimation, star identification and attitude calculation. There are lots of star centroid estimation methods [3][4][5], which are applied to calculate the centroid of stars. These methods play an important role in improving the precision of attitude determination for star sensors.…”

Sieve Search Centroiding Algorithm for Star Sensors

“…These noise processes are classified as the following, based on their origins: equipment noise and background noise. Optimizing the optical assembly to limit the displacement of star images, particularly imprinted at the edge of the FOV frames in [ 20 ], and predicting systematic errors using machine learning in [ 21 ], are two of the recent developments to compensate for equipment fidelity. To account for temporarily bright pixels in the sensor array, ref.…”

Adaptive star extraction algorithm in high dynamic

“…So the second method is to suppress the background noise and improve the accuracy of image segmentation by estimating the background noise more accurately. The methods to suppress the background noise include wavelet transform [6] , morphological filtering [7] , frequency domain filtering [8] and so on. The star trailing length is different at different angular velocities.…”