Random Star Recognition Algorithm Based on Image Total Station and Its Application to Astronomical Positioning

Abstract

To aid astronomical research in the identification and positioning of random stars in unfamiliar environments, this paper proposes a random star identification algorithm that does not require approximate station position. Rather, an observer calculates the angular distance between three selected stars at a unified time and forms an observation triangle using the angular distance difference method to match the observation triangle to a navigation triangle in a database of navigation stars. The detailed identification process is given in the article, and the distribution of the selected stars in the experiment is depicted intuitively. The measured data show that the success rate of random star recognition was as high as 100% when the observation interval between stars did not exceed 3 min. The article introduces the experiment and calculation of astronomical positioning by the identified stars, which realizes the identification and positioning of random stars without requiring approximate station position. We used 12, 8, or 4 stars with good spatial geometry as a positioning star group and analyzed the internal accord accuracy (Std) and external accord accuracy (Rms) of the positioning results of different stars. Multiple sets of experimental data show that 4 random stars with good spatial geometric distribution allowed for solving identification and positioning tasks with high efficiency and precision. Finally, four main sources of positioning error were analyzed: instrument angle measurement error, time error, star centroid extraction error, and positioning model error. The identification method proposed in this paper does not rely on station location information and has application value for rapid astronomical measurements in harsh environments, such as cloudy weather and cities with bright lights at night. In addition, the method can theoretically be used anywhere in the world.