# A Voting-Based Star Identification Algorithm Using a Partitioned Star Catalog
## 1 Introduction
Stars are coarsely identified first within a constrained range of the star catalog, and then stars are finely identified based on the results of the coarse identification. [PERSON] et al. [15] introduced a method to improve identification speed through linked-list matching. [PERSON] et al. [16] used a hash table to enhance retrieval speed. The most fundamental pattern recognition method is the grid algorithm [17], noted for its robustness against position errors. However, the need to rotate the star image during star identification significantly impacts the speed of the algorithm. Many scholars have developed different pattern recognition methods based on the grid algorithm, which can be broadly categorized into two types: One establishes different pattern features to improve the performance of the grid algorithm. [PERSON] et al. [18] improved the grid algorithm based on redundant coding and [PERSON] et al. (2019) introduced a grid algorithm combined with angular distance. [PERSON] et al. (2010) and [PERSON] et al. (2011) suggested using matrix singular values as pattern features. [PERSON] et al. (2012), [PERSON] et al. (2013), [PERSON] et al. (2014), [PERSON] et al. (2015), and [PERSON] et al. (2016) introduced methods using vectors composed of star positions as pattern features. [PERSON] et al. (2017) and [PERSON] et al. (2018) employed Hamming distance as a pattern feature, while [PERSON] et al. (2019) used Euclidean distance as a pattern feature for identification. The other uses different star image segmentation methods from the grid algorithm. [PERSON] et al. (2010) initially proposed a polygonal grid algorithm that segments the star image using polygonal grids. Following this, [PERSON] et al. (2010) introduced a radial and circular grid algorithm that segments the star image using radial and circular grids. [PERSON] et al. (2015), [PERSON] et al. (2013), [PERSON] et al. (2014), and [PERSON] et al. (2015) proposed improvements based on the radial and circular grid algorithm. Notably different from the grid algorithm, the radial and circular algorithm features rotational invariance, thus eliminating the need to rotate the star image during identification and significantly enhancing identification speed. With the rapid development and widespread application of neural network algorithms, lots of scholars have proposed star identification algorithms based on various network structures. [PERSON] et al. (2016) used an LVQ network structure for star identification. [PERSON] et al. (2017) employed an ant colony algorithm, [PERSON] et al. (2018) utilized an RPNet network structure, and [PERSON] et al. (2019) applied a CNN structure for star identification. [PERSON] et al. (2014) adopted a simple network structure, [PERSON] et al. (2017) implemented a Hierarchical CNN structure, and [PERSON] et al. (2019) applied a 1D-CNN structure in star identification. Comparative analysis of various star identification algorithms reveals the following conclusions ([PERSON] et al., 2019): Subgraph isomorphism methods have relatively low algorithmic complexity and strong portability in embedded systems, but low feature dimensionality often leads to redundant and false matching, and they require a large number of feature matches, resulting in slow identification speed. Pattern recognition methods have moderate algorithmic complexity and portability, but the construction of pattern features is limited by the number of stars in the star image. Insufficient stars can lead to redundant and false matching during the feature matching. Neural network methods have high algorithmic complexity and poor portability in generic star sensor platforms, and the performance is greatly influenced by the star dataset, leading to low stability and weak robustness. In light of the shortcomings present in each category of star identification algorithms, and after considering the performance and portability, we propose a voting-based star identification algorithm using a partitioned star catalog based on subgraph isomorphism methods.