Abstract
Star tracker is an important instrument of measuring a spacecraft’s attitude; it measures a spacecraft’s attitude by matching the stars captured by a camera and those stored in a star database, the directions of which are known. Attitude accuracy of star tracker is mainly determined by star centroiding accuracy, which is guaranteed by complete star segmentation. Current algorithms of star segmentation cannot suppress different interferences in star images and cannot segment stars completely because of these interferences. To solve this problem, a new star target segmentation algorithm is proposed on the basis of mathematical morphology. The proposed algorithm utilizes the margin structuring element to detect small targets and the opening operation to suppress noises, and a modified top-hat transform is defined to extract stars. A combination of three different structuring elements is utilized to define a new star segmentation algorithm, and the influence of three different structural elements on the star segmentation results is analyzed. Experimental results show that the proposed algorithm can suppress different interferences and segment stars completely, thus providing high star centroiding accuracy.
Highlights
A spacecraft’s attitude plays an important role in celestial navigation and attitude control
In considering the advantages and disadvantages of the discussed methods, this study proposes a new star target segmentation (NSTS) algorithm based on the characteristics of stars and different interferences
The results show that NSTS is faster than new top-hat transform (NWTH) and Arbabmir’s algorithm, which result from the decrease in the size of the margin structuring element, thereby reducing the amount of calculation and increasing the computational speed
Summary
A spacecraft’s attitude plays an important role in celestial navigation and attitude control. Given the uneven illumination in star images with the moon, the histogram of these images is inseparable into distinct partitions. It cannot remove interferences and cannot extract stars accurately. Mao proposed a local thresholding algorithm based on the Niblack algorithm;[9,10] it can remove uneven illumination interference, but the star segmentation is incomplete and cannot guarantee centroiding accuracy. In considering the advantages and disadvantages of the discussed methods, this study proposes a new star target segmentation (NSTS) algorithm based on the characteristics of stars and different interferences.
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