# Star Identification Algorithm Based on Dynamic Distance Ratio Matching  
[PERSON]  
1 School of Aeronautics and Astronautics, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Shanghai 201203, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Shanghai Satellite Network Research Institute Co., Ltd., Floor 3, Building 2, No. 200, Zhangheng Road, China (Shanghai) Pilot Free Trade Zone, Shanghai 201203, China; [EMAIL_ADDRESS] (C.S. ); [EMAIL_ADDRESS] (L.B. ); [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (R.Z. ); [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (W.Z.) [PERSON]  
2 Shanghai Satellite Network Research Institute Co., Ltd., Floor 3, Building 2, No. 200, Zhangheng Road, China (Shanghai) Pilot Free Trade Zone, Shanghai 201203, China; [EMAIL_ADDRESS] (C.S. ); [EMAIL_ADDRESS] (L.B. ); [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (R.Z. ); [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (W.Z.) [PERSON]  
2 Shanghai Satellite Network Research Institute Co., Ltd., Floor 3, Building 2, No. 200, Zhangheng Road, China (Shanghai) Pilot Free Trade Zone, Shanghai 201203, China; [EMAIL_ADDRESS] (C.S. ); [EMAIL_ADDRESS] (L.B. ); [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (R.Z. ); [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (W.Z.) [PERSON]  
2 Shanghai Key Laboratory of Satellite Network, Floor 6, Building 1, No. 491 Yindong Road, Shanghai 201203, China 23344 State Key Laboratory of Satellite Network, Floor 6, Building 1, No. 491 Yindong Road, Shanghai 201203, China 3344 State Key Laboratory of Satellite Network, Floor 6, Building 1, No. The triangular algorithm proposed by [PERSON] et al. [6] utilizes the angular distances between three stars as features for identification. This method is relatively fast but exhibits some redundancy. [PERSON] et al. [7] extended this approach to include four star points, further reducing redundancy in the identification process. [PERSON] et al. [8] introduced an algorithm based on the navigation star field and K-vectors, accelerating the algorithm using a K-vector lookup table. [PERSON] et al. [9] proposed converting the star image from Cartesian to polar coordinates to achieve scale and rotation invariance in the identification algorithm. [PERSON] et al. [10] developed a grid-based algorithm for star pattern matching by rasterizing the star image as a feature; however, this algorithm performs poorly under small disturbances such as varying star magnitudes and positional noise. [PERSON] et al. [11] replaced the hard template matching in the original raster matching process with a cost function that considers the differences between the measured pattern and database patterns, employing the relative magnitudes of stars as weights to enhance robustness against positional and magnitude noise. [PERSON] et al. [12] introduced the Polestar algorithm, using one star as a reference to calculate angular distances to neighboring stars, forming a specific binary vector and applying a lookup table structure combined with a voting method for identification. Subsequently, [PERSON] [13] utilized a stepwise identification algorithm, performing initial matching with radial features and subsequent matching with angular features. However, like the grid algorithm, the choice of the starting edge in angular features is susceptible to noise. [PERSON] et al. [14] proposed using the angles between neighboring star vectors as dynamic angular features to mitigate the impact of starting edge selection under noisy conditions. [PERSON] et al. [15] employed a rotation-invariant additive vector as identification features, selecting the best match via voting; however, this method also heavily relies on the selection of the starting star. The aforementioned identification processes are overly dependent on the starting star, leading to inadequate identification precision in the presence of significant positional noise or numerous false stars.