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https://doi.org/10.62836/jitp.v1i1.174
Wang, M., Zhang, H. . ., & Zhou, N. (2024). Star Map Recognition and Matching Based on Deep Triangle Model. Journal of Information, Technology and Policy, 1–18. https://doi.org/10.62836/jitp.v1i1.174
Volume 2, Issue 1, 2024
Copyright (c) 2024 Meng Wang, Huitao Zhang, Ning Zhou
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Star Map Recognition and Matching Based on Deep Triangle Model

The star sensor is the key component of Celestial Navigation. It measures the autonomous attitude of navigation bodies by observing stars. And it conducts image collection, preprocessing, feature extraction and matching recognition. Aimed to implement the latter two procedures, we first estimate the coordinate of the point which is the intersection point of the optical axis and the celestial sphere. We employ geometrical knowledge to get the relationship between the intersection point and given projection distances. When distances are unknown, we use Newton’s method to approach the exact coordinate of the intersection point. Based on our coordinate calculation method, we are required to find a principle for improving the accuracy of the coordinate. We first establish a projection screening model to obtain star maps. Then we establish four coordinate systems, i.e., the celestial coordinate system, the star sensor coordinate system, the image coordinate system and the pixel coordinate system. Taking the star map at the north celestial pole as an instance, we finish the transformation of coordinate between different systems and search for the factors affecting accuracy of coordinate. Ultimately, we draw the conclusion that the coordinate accuracy improves, when selected stars projection close the centroid of the photosensitive surface. Aimed to implement the matching recognition, we establish a novel feature extraction and matching model. We take the angle between stars and three of their nearest stars as the feature of the central star. Then we extract the feature matrix of the given star table as the feature database. Using the same way, we get the feature matrix of four–star maps. To achieve the last step of matching recognition, we compare the feature matrix of star maps with the given navigation stars. During the process, we employ DBScan clustering algorithm to implement the matching recognition process. We select the cluster center that satisfies the maximum number of matches as the actual location of the identified star map.

star map recognition; feature extraction; transformation of coordinate system; computer vision

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Supporting Agencies

  1. Funding: Not applicable.