Star sensor star map identification method based on convolutional neural network

A convolutional neural network and star map recognition technology, applied in instruments, measuring devices, surveying and navigation, etc., can solve problems such as unsatisfactory running speed, achieve short search time, strong robustness, anti-noise and anti-counterfeiting star performance strong effect

Active Publication Date: 2018-04-06
CHANGZHOU INST OF TECH
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Problems solved by technology

The star map recognition algorithm based on triangle eigenvectors and eigenvalues ​​uses triangle eigenvectors and eigenvalues ​​as recognition features, improves the anti-interference performance of the recogni

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  • Star sensor star map identification method based on convolutional neural network
  • Star sensor star map identification method based on convolutional neural network
  • Star sensor star map identification method based on convolutional neural network

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Embodiment 1

[0068] The SAO star catalog is selected as the original star catalog, the limiting magnitude is 5.2, the field of view is 20°×20°, the resolution of the star sensor detector is 1024×1024, and the focal length is 43.56mm. After filtering the original star catalog, 1607 stars were obtained, all of which were selected as navigation stars. When the optical axis points to the coordinates (120°, 20°) on the celestial sphere, the distribution of navigation stars in the field of view is as follows: image 3 As shown, there are 16 stars in total, and the detailed information is shown in Table 1.

[0069] After traversing the constellations and clustering the entire celestial sphere, their constellation numbers are shown in Table 1. Among them, the constellation composed of 7 stars with serial numbers of 9, 10, 12, 13, 14, 15, and 16 has the largest number of stars, and its number is 410.

[0070] When the direction of the optical axis remains unchanged and the field of view is rotated...

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Abstract

The invention discloses a star sensor star map identification method based on a convolutional neural network. The star sensor star map identification method comprises the following steps: carrying outstar filtration treatment on the original star catalog, establishing a navigation star catalog, carrying out statistic on constellations of the whole celestial sphere navigation stars, and numberingthe constellations, wherein a sample library is composed of a simulation star map and numbers of the constellations corresponding to the most stars; replacing the original star map with a sparse matrix, inputting a sample library star map into the convolutional neural network, and carrying out training; carrying out star image extraction on a star map obtained by shooting, converting the extractedstar image into a sparse matrix, then inputting into the convolutional neural network, carrying out coarse attitude star map identification, and obtaining a rough orientation; and identifying a fixedstar in a view field by applying a local sky area star map identification algorithm. The star sensor star map identification method disclosed by the invention has the advantages that a trained convolutional neural network is adopted for realizing coarse attitude whole celestial sphere star map identification, the navigation star catalog does not need to be searched, and the local sky area star map identification only needs to search a small part of a database; and the convolutional neural network has the capability of autonomously extracting characteristics of the original map and has stronganti-noise and anti-fake-star performance when being applied to star map identification.

Description

technical field [0001] The invention belongs to the technical field of celestial navigation and relates to a star map recognition method for a star sensor. Background technique [0002] Star map recognition algorithm is one of the core technologies of star sensors. In recent decades, a lot of research has been done on the all-day autonomous star map recognition of spacecraft at home and abroad, and many algorithms have been proposed, mainly including: angular distance algorithm, triangle algorithm , grid method, binary tree method and algorithm based on pyramid model, etc. [0003] Li Xinlu, Yang Jinhua and others from the College of Optoelectronic Engineering, Changchun University of Science and Technology, in order to overcome the redundant matching problem caused by the low feature dimension of triangles in the triangle recognition algorithm, established a declination zone angular distance feature library and selected optimized triangle constraints. Improved triangle met...

Claims

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Application Information

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IPC IPC(8): G01C21/02G01C21/20
CPCG01C21/025G01C21/20
Inventor 吴峰朱锡芳徐也相入喜于秋阳缪志康吴涛
Owner CHANGZHOU INST OF TECH
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