Star sensors star point recognition algorithms can generally be divided into three categories:
Firstly, star recognition algorithms based on constellation features utilize feature quantities for recognition, which are determined by the positional relationship between stars.
The earliest star recognition algorithm based on constellation features was the triangle algorithm proposed in the 1970s. As the name suggests, it utilizes the triangle features composed of stars for star recognition. Although it has been developed for many years, it and its improved methods are still the most commonly used star recognition algorithms in engineering.
Compared to the earliest methods of simply using corner distance features, the triangle algorithm has higher feature dimensions and higher recognition accuracy. The disadvantage of triangle recognition algorithm is also quite obvious, that is, when the number of matching triangles is large, it is prone to incorrect matching, which leads to a decrease in recognition success rate. In addition to the triangle algorithm, there is also a matching group based algorithm that utilizes multiple bright stars as the main star, calculates the angular distance relationship between the main star and the auxiliary star, and achieves matching search with the star library. However, due to the uncertainty of the size of the matching group, only angular distance can be used as the basic parameter, which can easily lead to uncertainty in the recognition process. The matching group algorithm is also a commonly used recognition algorithm in engineering model tasks.
Pyramid recognition algorithm based on k-vector technology: k-vector technology can achieve fast query of star catalogs, thereby reducing the search times of navigation star catalogs and ultimately improving the speed of star point recognition. The pyramid algorithm selects four observation stars to form a pyramid feature graph, with one star as the vertex, and then the remaining three stars form a triangle and form a pyramid shaped tetrahedron with it. The disadvantage is that as the star pair information table increases, it will cause a decrease in the accuracy corresponding to the fitting curve, resulting in the best matching star pair not being within the range of angular error.
The grid recognition algorithm maps star coordinates onto a sparse matrix and achieves recognition through matrix matching. The advantages of grid algorithm are very obvious, as it has the advantages of small storage space requirements, fast recognition speed, and insensitivity to measurement errors. However, when the positioning error or corresponding magnitude error of a star is particularly large, its corresponding recognition success rate will quickly decrease. The “Elastic Gray Grid Algorithm” adds a virtual elastic template during recognition. The elastic grayscale grid algorithm can effectively improve the fault tolerance for magnitude and noise errors, thereby further improving the recognition rate. Using a circular grid instead of the initial square grid can solve the dependence of the square grid algorithm on reference stars, while also having high robustness to rotating environments. The KMP (Knuth Morris Pratt) star recognition algorithm is another manifestation of the grid algorithm. The KMP star recognition algorithm performs high pass filtering on the star map, generates 0-1 strings and corresponding formal matching patterns, and finally uses the KMP string search algorithm to complete the string recognition of the star map.
This is commonly known as a recognition method based on neural networks. Scholars have introduced neural networks into star recognition and proposed a star recognition method based on fuzzy neural networks. This algorithm uses the angular distance information of the three corresponding sides that make up the triangle, then trains and learns the navigation triangle library, and finally uses the learned neural network structure to complete star map recognition. The advantages of this algorithm lie in its high recognition success rate and fast recognition speed, but its disadvantages are also obvious: slow training and learning speed, requiring a large number of samples for training, and the accuracy of recognition can easily vary due to differences in training set size and training time. Additionally, it is a significant test for the algorithm implementation platform. The star recognition method based on genetic algorithm first selects a main star, and then uses encoding to complete the classification of the star diagonal distance between the main star and its companion star, as well as the angle between stars. In addition, two sets of star maps corresponding to the star angular distance error and angle error sum are specified as fitness functions, and then coarse and fine positioning searches are carried out twice to increase the mutation factor in the coarse positioning stage, Reduce the variation factor during the fine localization stage.
The three major types of methods mentioned above are common star recognition algorithms, but most algorithms accumulate errors under the interference of transient effects and cannot achieve effective and continuous star recognition. In recent years, some experts have proposed new recognition methods for star point recognition under noise interference, but these algorithms still cannot achieve high recognition success rates under a large amount of transient noise interference.
Star sensors are currently the most accurate among attitude sensors, with an accuracy of up to sub angular seconds. They calculate the corresponding three-axis attitude information based on the reference coordinate position of the star in the celestial coordinate system and the actual observation coordinates of the detector plane where the star is located, providing accurate basis for spacecraft attitude measurement and control systems. In addition, star sensors can also be used in fields such as long-range bomber navigation, ballistic guidance, and ship attitude measurement, with broad application prospects.
When the star sensor points towards a certain sky area in a specific attitude, the starlight signal of the target star is imaged on the image sensor through an optical system. The imaging system captures the starry sky image pointed by the current star sensor’s line of sight, and then sends it to the signal processing circuit. By extracting the position coordinates and magnitude information of the star on the detector plane, the star map recognition algorithm finds the corresponding match of the observed star in the navigation database, Finally, based on the direction vectors between these matched star pairs, the three-axis attitude of the star sensor is calculated, and the attitude information of the spacecraft is obtained.
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