Abstract
Images of starfields collected by a projective camera are useful for a variety of scientific and engineering purposes. This utility is exemplified by star trackers, which are amongst the most commonly used sensors for determining the attitude of modern spacecraft. While the literature on star identification and star-based attitude determination is extensive, most algorithms are developed in an ad hoc manner. This work provides a comprehensive and systematic framework for invariant-based star identification and shows most past star identification algorithms to be special cases within this framework. The new star identification framework is found to motivate new problems in attitude determination and sensor self-calibration. Specifically, new algorithms are presented for simultaneous attitude determination and camera calibration for a generic wide field-of-view sensor using a single starfield image. In the special case where camera focal length is the only unknown calibration parameter, attitude determination performance of the new algorithm is indiscernible from a perfectly calibrated camera.
Highlights
There are a variety of situations where it is necessary to autonomously recognize an asterism in a digital image
The present manuscript is motivated by the spacecraft attitude determination application, which has seen numerous star identification algorithms proposed since the 1970s [4], [5]
GEOMETRY OF STAR OBSERVATIONS WITH A PROJECTIVE CAMERA Most modern star catalogs (e.g., Hipparcos [16], [17], Gaia [18], [19]) describe the direction to star by a pair of angles in the International Celestial Reference Frame (ICRF) [20]–[22]
Summary
There are a variety of situations where it is necessary to autonomously recognize an asterism (i.e., a star pattern) in a digital image. Numerical comparisons of some existing algorithms may be found elsewhere [4], [5], [10] The contribution of this manuscript is not the development of new star identification algorithms, but a better theoretical framework for understanding how the vast majority of these algorithms function. While the contribution of this manuscript in the area of star identification is a theoretical framework, the contribution in the area of attitude determination and self-calibration is a set of novel algorithms. The performance of these algorithms are VOLUME 9, 2021 verified with numerical simulations and compared to some of the most popular algorithms in use today
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