Abstract
Stray light, such as sunlight, moonlight, and earth-atmosphere light, can bring about light spots in backgrounds, and it affects the star detection of star sensors. To overcome this problem, this paper proposes a star detection algorithm (CMLCM) with multidirectional local contrast based on curvature. It regards the star image as a spatial surface and analyzes the difference in the curvature between the star and the background. It uses a facet model to represent the curvature and calculate the second-order derivatives in four directions. According to the characteristic of the star and the complex background, it enhances the target and suppresses the complex background by a new calculation method of a local contrast map. Finally, it divides the local contrast map into multiple 256 × 256 sub-regions for a more effective threshold segmentation. The experimental results indicated that the CMLCM algorithm could effectively detect a large number of accurate stars under stray light interference, and the detection rate was higher than other compared algorithms with a lower false alarm rate.
Highlights
A star sensor [1] is a high-precision attitude measurement device
The stray light will affect the size of the star spot and the energy distribution and form a light spot on the star image. This interferes with the normal operation of star detection [3,4,5] and affects the attitude measurement accuracy of star sensors
Under complex backgrounds, the extraction accuracy is affected by stray light
Summary
A star sensor [1] is a high-precision attitude measurement device. It is widely used in various space vehicles, including space stations, mars probes, and various satellites in different orbits. The stray light will affect the size of the star spot and the energy distribution and form a light spot on the star image. This interferes with the normal operation of star detection [3,4,5] and affects the attitude measurement accuracy of star sensors. The background with stray light can be called a complex background
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