Motion-blurred star acquisition method of the star tracker under high dynamic conditions.

The star tracker is one of the most promising attitude measurement devices used in spacecraft due to its extremely high accuracy. However, high dynamic performance is still one of its constraints. Smearing appears, making it more difficult to distinguish the energy dispersive star point from the noise. An effective star acquisition approach for motion-blurred star image is proposed in this work. The correlation filter and mathematical morphology algorithm is combined to enhance the signal energy and evaluate slowly varying background noise. The star point can be separated from most types of noise in this manner, making extraction and recognition easier. Partial image differentiation is then utilized to obtain the motion parameters from only one image of the star tracker based on the above process. Considering the motion model, the reference window is adopted to perform centroid determination. Star acquisition results of real on-orbit star images and laboratory validation experiments demonstrate that the method described in this work is effective and the dynamic performance of the star tracker could be improved along with more identified stars and guaranteed position accuracy of the star point.

[1]  Carl Christian Liebe,et al.  Accuracy performance of star trackers - a tutorial , 2002 .

[2]  A. Murat Tekalp,et al.  Maximum likelihood image and blur identification: a unifying , 1990 .

[3]  Yitzhak Yitzhaky,et al.  Identification of blur parameters from motion-blurred images , 1996, Optics & Photonics.

[4]  M. Cannon Blind deconvolution of spatially invariant image blurs with phase , 1976 .

[5]  Domenico Accardo,et al.  Enhancement of the centroiding algorithm for star tracker measure refinement , 2003 .

[6]  Jian Liu,et al.  Facet-based star acquisition method , 2004 .

[7]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[8]  Zhou Wang,et al.  Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .

[9]  Ting Sun,et al.  Optical System Error Analysis and Calibration Method of High-Accuracy Star Trackers , 2013, Sensors.

[10]  Timothy A. Clarke,et al.  Comparison of some techniques for the subpixel location of discrete target images , 1994, Other Conferences.

[11]  Richard Hornsey,et al.  Determining star-image location: A new sub-pixel interpolation technique to process image centroids , 2007, Comput. Phys. Commun..

[12]  N S Kopeika,et al.  Comparison of direct blind deconvolution methods for motion-blurred images. , 1999, Applied optics.

[13]  G. Wahba A Least Squares Estimate of Satellite Attitude , 1965 .

[14]  J. Alex Stark,et al.  Adaptive image contrast enhancement using generalizations of histogram equalization , 2000, IEEE Trans. Image Process..

[15]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[16]  Sung-Jea Ko,et al.  Center weighted median filters and their applications to image enhancement , 1991 .

[17]  Lei Guo,et al.  Blurred Star Image Processing for Star Sensors under Dynamic Conditions , 2012, Sensors.

[18]  Andreas E. Savakis,et al.  Blur identification by residual spectral matching , 1993, IEEE Trans. Image Process..

[19]  C. Tyler,et al.  Bayesian adaptive estimation of psychometric slope and threshold , 1999, Vision Research.