Star Catalog Based on Triangulation Networks, Angular Distance Weights, and Approximate Longitude Models

This article investigates the problem of the searches and time for stars identification, in which star magnitude, position noise, missing, or false star are considered. A novel star catalog based on angular distance, approximate longitude, and triangulation network is developed, which can accelerate the matching. The preliminary selection of stars is completed by magnitude and projection model. The final star catalog is constructed by Delaunay triangulation, magnitude, and angular distance. A longitude approximation model is proposed by star sensor visual axis and distance of the star on the image. The simulation results show that the proposed algorithm has anti-interference ability to the false star, missing star, and position noise. In addition, a semi-physical simulation experiment of trajectory rocket launch is created to demonstrate the effectiveness of the proposed matching method. Experimental results show that a single matching requires on average 2.1 searches, and the matching efficiency of the proposed method can be greatly improved compared with the existing algorithm.