Fast star identification algorithm based on multi-feature matching

Jun, 王军 Wang; Xin, 何昕 He; Zhong-hui, 魏仲慧 Wei; You, 吕 游 L; Zhi-ya, 穆治亚 Mu

doi:10.3788/ope.20192708.1870

Cited by 4 publications

(1 citation statement)

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“…Like other evolutionary algorithms, the improved artificial bee colony algorithm adopts the idea of "survival of the fittest" of Darwin's evolutionary theory to select and retain individuals to ensure that the algorithm continues to evolve towards the global optimal solution [4] . Therefore, the new individuals obtained in the previous section need to be compared with the scout bee individual X1 in terms of gain value, and the one with better gain value will be selected to enter the next generation of the leader bee population according to formula (3).…”

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confidence: 99%

A study on the method of recognizing star maps in the all-sky region

Wang,

Zhan,

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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In order to improve the chart recognition speed and the recognition success rate of the large field-of-view high-sensitivity star sensitizer, an improved artificial bee colony algorithm combined with the three anti-perturbation parameters is proposed as a chart recognition method. The method is based on the improved artificial bee colony algorithm to find the optimal paths for all the stars in the main star feature region; then, the optimal path length is used to find addresses in the navigation feature library, and the interstellar angular distances of the first three navigation stars in the optimal paths, d12/d23 , are taken as the main recognition features for matching and identification; finally, the three perturbation parameters are taken as the auxiliary recognition features to reduce the redundancy of the matching and to serve as a validation of the recognition.

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confidence: 99%