Sponsor(s): Changchun Institute of Optics,Fine Mehcanics and Physics,Chinese Academy of Sciences 、China Instrument and Control Society
12 issues per year
Current Issue: Issue 11, 2019
Optics and Precision Engineering is supervised by Chinese Academy of Sciences and co-sponsored by Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CAS), China Instrument and Control Society and Chinese Society of Micro-nano Technology. The journal was launched in 1959 with the name of "Optics Mechanics." Currently, it has become one of the leading Chinese journals in optics in China, along with an increasing influence and a large circulation nationally. Especially in the field of interdisciplinary among modern applied optics, micro-nano technique and precision engineering, the journal is highly praised by both domestic and foreign experts, including Prof. Charles H. Townes, Nobel laureate. It aims at promoting the generation, application and archiving of knowledge in optics and disseminating it worldwide. Its scope covers modern applied optics, micro/nano technology and fine mechanics, and information sciences. The journal is included in CA, SA, JST, Pж(AJ), EI, CSCD.
Optics and Precision Engineering,2019,Vol 27,No. 11
The star identification algorithm is a key technology for star sensors. Traditional star identification algorithms, such as triangle algorithm, polygon algorithm, and other improved algorithms mainly consider the star diagonal distance as an identification feature. The accuracy of the calculated star diagonal distance is dependent on the calibration accuracy of the focal length f of the charge-coupled device (CCD) camera. These identification algorithms cannot work properly if the calibration accuracy is insufficient or if the focal length of the camera changes significantly owing to environmental conditions. This paper proposed a new star identification algorithm based on the similar triangle using the similar triangles between the observed triangle and the triangle consisting of image points of the CCD camera. Because the identification process was not dependent on the focal length, it still had a large error. Finally, simulation verification was carried out with the Monte Carlo method. The results show that the recognition rate of the proposed algorithm remains unchanged, with a large focal length error. The proposed algorithm has an average recognition rate of 95.2% while the recognition speed can reach 5.3 ms. In contrast, the average recognition speed of the traditional triangle algorithm is approximately 7.6 ms under the same experimental conditions. The proposed algorithm and traditional triangle algorithm has a recognition rate of 93.3% and 86.5%, respectively, when the image position error is 0.5 pixels. Compared with the traditional triangle algorithm, the proposed algorithm has a higher speed and improved robustness.