A Star Identification Graph Algorithm Based on Angular Distance Matching Score Transfer

Wei, Yuheng; Wei, Xinguo; Liu, Hao; Li, Jian

doi:10.1109/jsen.2024.3350089

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…In our experiments, when the parameters were set to their typical values (i.e., the typical star spot extraction error was

pixel, the number of image frames was

, and the number of star spots was

), the SCWC achieved

, its relative error was 0.11%, and its direction error corresponding to the edge of the field of view was approximately 40″. For a typical star sensor with a sensitive magnitude error of less than 0.5 Mv, the full-sky star identification probability of the traditional star map identification algorithm [ 10 ] was still better than 98%. Thus, for a star sensor with typical parameters, the intrinsic parameters determined by on-orbit calibration could be directly used in a traditional star map identification algorithm.…”

Section: Simulation and Analysis Resultsmentioning

confidence: 99%

“…The implementation of attitude-independent calibration methods is based on the premise that the relationship between star spots and guide stars has been established, that is, star identification is completed before the intrinsic parameters of the star sensor are determined; these methods are also called dimensionless (or non-calibration) star map identification algorithms [ 8 , 9 ]. Unlike traditional star map identification algorithms, where the minimum identifiable star map is a star pair [ 10 ], dimensionless star map identification algorithms take a star triangle as the minimum identifiable star map. A disadvantage of these algorithms is the requirement for a large database and complex data retrieval algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Self-Calibration for Star Sensors

Fu,

Lin,

2024

Sensors

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Aiming to address the chicken-and-egg problem in star identification and the intrinsic parameter determination processes of on-orbit star sensors, this study proposes an on-orbit self-calibration method for star sensors that does not depend on star identification. First, the self-calibration equations of a star sensor are derived based on the invariance of the interstar angle of a star pair between image frames, without any requirements for the true value of the interstar angle of the star pair. Then, a constant constraint of the optical path from the star spot to the center of the star sensor optical system is defined to reduce the biased estimation in self-calibration. Finally, a scaled nonlinear least square method is developed to solve the self-calibration equations, thus accelerating iteration convergence. Our simulation and analysis results show that the bias of the focal length estimation in on-orbit self-calibration with a constraint is two orders of magnitude smaller than that in on-orbit self-calibration without a constraint. In addition, it is shown that convergence can be achieved in 10 iterations when the scaled nonlinear least square method is used to solve the self-calibration equations. The calibrated intrinsic parameters obtained by the proposed method can be directly used in traditional star map identification methods.

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“…In our experiments, when the parameters were set to their typical values (i.e., the typical star spot extraction error was

pixel, the number of image frames was

, and the number of star spots was

), the SCWC achieved

Section: Simulation and Analysis Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Calibration for Star Sensors

Fu,

Lin,

2024

Sensors

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Maximum radial pattern matching for minimum star map identification

Fu,

Li,

Lin

et al. 2024

EURASIP J. Adv. Signal Process.

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This paper proposes an all-sky star map identification algorithm that can simultaneously achieve high identification probability, low algorithm complexity, and small databases for well photometric and intrinsic parameters-calibrated star sensors. The proposed algorithm includes three main steps. First, a binary radial pattern table is constructed offline. Then, the maximum value matching of the radial pattern is performed between the star spots and the guide stars, and the star pairs (i.e., the minimum star map) after radial pattern matching undergo a coarse matching through angular distance cross-validation. Finally, a reference star map is designed based on the identified star pairs, and the matching of all the star spots in the field of view is realized. Simulation and analysis results show that the database required by the proposed algorithm for 5,000 guide stars is not larger than 200 KB. Also, when false and missing star spots account for 50% of all guide stars and the star spot extraction error is 0.5 pixel (the corresponding pointing error is 26″), the average star map identification time of the proposed algorithm is less than 2 ms, and its identification probability is higher than 98%. The results demonstrate that the proposed algorithm performs better than similar algorithms.

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A Star Identification Graph Algorithm Based on Angular Distance Matching Score Transfer

Cited by 2 publications

References 40 publications

Self-Calibration for Star Sensors

Self-Calibration for Star Sensors

Maximum radial pattern matching for minimum star map identification

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