# A Voting-Based Star Identification Algorithm Using a Partitioned Star Catalog
## 1 Introduction  
A star sensor is one kind of high-precision, highly reliable autonomous attitude measurement instrument that uses stars as measurement objects and is now widely used in space payloads such as spacecraft [1]. When first activated or experiencing being lost in space, the star sensor needs to image the starry sky, and achieves attitude measurement after star extraction, centroid positioning, and star identification. The primary workingprinciple of star sensors involves imaging the starry sky using an optical system and image sensor, extracting stars and locating centroids to obtain the position and brightness information of stars on the image sensor, and identifying corresponding navigation stars in the star catalog through a star identification algorithm. Then, the attitude of the star sensor relative to the inertial coordinate system is measured [2]. With the rapid development of aerospace technology and the increasing maneuverability of spacecraft, the demand for star sensors with a high attitude update rate and precision is quite urgent. Since star identification represents the algorithmically most complex and time-consuming step in the working process of star sensors [3], heightened demand for the identification speed and rate of the star identification algorithm has been put forward. Additionally, as the space environment becomes increasingly complex and the need to provide real-time, accurate warnings for various non-cooperative targets grows, the demand for the detection sensitivity of star sensors also increases, leading to a rapid increase in the number of high-magnitude navigation stars in the star catalog [4], severely impacting the speed and rate of star identification. Consequently, star identification algorithms with fast identification speed and a high identification rate are the crucial technology in the current research field of star sensors. Depending on the recognition strategy, star identification algorithms are primarily divided into three categories: subgraph isomorphism, pattern recognition, and neural network methods [5]. The subgraph isomorphism methods match the geometrics of stars in the star image with navigation stars in the star catalog. The pattern recognition methods use pattern features of stars in the star image with navigation stars in the star catalog. The neural network methods employ a trained network structure to match stars in the star image with navigation stars in the star catalog based on distribution features [6]. The most fundamental method of the subgraph isomorphism methods is the triangle algorithm, characterized by its requirement for fewer stars and strong portability in embedded systems. Theoretically, star identification can be performed with no fewer than three stars in the star image. However, the features of the triangle algorithm are simplistic, easily leading to redundant and false matching during star identification, and a large number of feature matches influences the identification speed. Plenty of scholars have proposed different subgraph isomorphism methods based on the triangle algorithm, mainly divided into two categories: One involves increasing the dimensionality of features to enhance the identification rate. [PERSON] et al. [7] introduced the pyramid algorithm, which added an angular distance feature to the triangle algorithm. [PERSON] et al. [8] proposed the double-triangle algorithm, doubling the feature dimensionality compared to the triangle algorithm. [PERSON] et al. [9] used a polygonal algorithm to increase feature dimensionality. The other involves reducing the number of feature matches to improve identification speed. [PERSON] et al. [10], [PERSON] et al. [11], [PERSON] et al. [12], [PERSON] et al. [13], and [PERSON] et al. [14] each proposed different subgraph isomorphism algorithms, but their star identification strategies are broadly similar, dividing star identification into coarse and fine identification stages.