During the operation of the star sensor in orbit, due to the transient effects caused by space radiation, a large amount of transient noise is easily generated on the detector target surface. Famous spatial radiation belts include the South Atlantic Anomaly (SAA) region, where traditional star sensors may experience ineffective attitude and switch to LIS mode due to a large amount of transient noise interference when passing through the SAA region. When the star sensor enters LIS mode, it is difficult to accurately capture and enter tracking mode under the interference of a large amount of transient noise, which is also a problem that is difficult to avoid for most star sensors models during orbital operation. At present, most of the star recognition algorithms studied by domestic and foreign experts focus on feature selection and the improvement of star recognition ability in conventional backgrounds. A few star recognition algorithms for transient noise [116] are limited by the amount of noise and still cannot achieve good recognition results. To solve this problem, it is necessary to propose a star sensor star point recognition algorithm suitable for large amounts of transient noise interference, which can complete star recognition again under the interference of transient noise and output accurate attitude information.
Dr. Lu Kaili (Institute of Optics and Electronics, Chinese Academy of Sciences) proposed a fast star recognition algorithm (FSI) for star sensors. Unlike traditional corner distance matching algorithms, this algorithm mainly utilizes the statistical approach of star repetition to achieve the positioning and recognition of the main star. This algorithm first targets the characteristics of transient noise and completes rough noise suppression through preprocessing; Then, a custom star attribute table that can be quickly queried was constructed, and the angular distance between the main star and neighboring stars was calculated. The K-vector method was used to search for the star pair list corresponding to the angular distance in the star attribute table; Utilize a fast repeated star search technique to achieve preliminary positioning of the main star star; Finally, the correct information of the main star within the field of view is determined through a dual screening method of field of view and angular distance.
(1) Star recognition problem of star sensors entering LIS mode under transient noise interference
When a star sensor passes through the SAA region, it is susceptible to transient effects in imaging devices (CMOS detectors) due to strong spatial radiation. Transient effects refer to the phenomenon where high-energy particles enter the sensitive layer of the imaging device, absorb energy, and generate electron hole pairs, resulting in transient noise on the detector. The manifestation of transient noise on the detector target varies depending on the direction in which high-energy particles enter the detector. The more perpendicular the angle of penetration, the closer the noise shape is to the star point; The more inclined the angle of insertion, the longer the trailing edge of the noise. As shown in Figure 4.2, transient noise exhibits different shape features in the star map. Transient noise has the characteristics of randomness and transience. The emergence of transient noise brings great difficulties to the star recognition process of star sensors.
(2) A Fast Star Recognition Algorithm (FSI) for Star Sensors
This algorithm can make the star sensor more effective in star point recognition under the interference of a large amount of transient noise. The FSI algorithm utilizes the randomness and transience of transient noise to eliminate diagonal noise in the process of single frame star point detection using scale information, and eliminates other similar star point noise by comparing the position and energy information of adjacent frame star points, thus achieving preliminary suppression of a large amount of noise; The FSI algorithm uses K-vector based multi star search technology to complete the final star recognition. The multi star search technology first constructs a custom star attribute table, then calculates the angular distance between the main star and neighboring stars, and uses the K-vector method to search for star pairs corresponding to the angular distance in the star attribute table. A fast repeated star search technology based on address index is used to achieve the initial positioning of the main star; Finally, the FSI algorithm determines the correct information of the main star in the field of view through a dual screening method of field of view and angular distance.
The discussion is about denoising methods for single pixel noise. The traditional engineering processing methods mainly use spatial filtering denoising algorithms. The main mechanism of spatial filtering denoising algorithms is smooth filtering, which includes mean filtering, box filtering, median filtering, and Gaussian filtering. These types of filtering algorithms usually have a smoothing effect on the background.
(1) Mean filtering
Mean filtering, as the name suggests, is to obtain the pixel mean of an image area, taking the current pixel as the center point, and calculate the surrounding 𝑛 × The statistical average of each pixel within the 𝑛 box. The advantage of mean filtering is that its calculation speed is particularly fast, but the resulting image is relatively blurry. The larger the filtering kernel, the more blurry the image becomes. Mean filtering has a good processing effect on Gaussian noise, but its effect on salt and pepper noise is average.
(2) Box filtering
The advantage of mean filtering is that its calculation speed is particularly fast, but the resulting image is relatively blurry. The larger the filtering kernel, the more blurry the image becomes. Mean filtering has a good processing effect on Gaussian noise, but its effect on salt and pepper noise is average.
(3) Median filtering
Median filtering is the process of obtaining the median value of other pixels in the neighborhood of the current central pixel to replace the current central pixel. Median filtering has a relatively good filtering effect on salt and pepper noise.
(4) Gaussian filtering
The principle of Gaussian filtering is to use the filtering kernel function for convolution operations.
The above-mentioned spatial filtering methods have a significant impact on the target during the denoising process, and may have fuzzy effects on the target during the smooth filtering process, thus having some drawbacks.
(5) Multi frame prediction method
A method of background prediction that accumulates background values through multiple frames and then removes the influence of noise through background subtraction. However, this method has requirements for the dynamic conditions of star sensors and is only suitable for low angular rates, and is mainly suitable for situations where the noise position is relatively fixed.
Scaling the correction domain at high speeds can adapt to higher dynamic speeds. As shown in Figure 1.4, during the process of star point movement, the processing frames of each frame are continuously used as background information. When the frames of multiple consecutive frames are combined into a set of backgrounds, noise removal can be achieved by background subtraction in the current frame. However, this method can still only handle fixed single pixel noise, and its suppression effect is poor for position random noise.
The algorithms mentioned above have limitations in practical applications, making it difficult to maintain the accuracy of star point centroid positioning while denoising.
Before conducting star point recognition, it is usually necessary to obtain star point position information in advance, which is called star point detection. According to the characteristics of star maps, the main tasks of star point detection include:
(1) Segmentation between star targets and background;
(2) The segmentation between a single star target and other stars.
Star point detection is a processing method that achieves threshold segmentation of targets and backgrounds in star maps. The specific steps of star detection include clustering, segmentation, labeling, and final centroid positioning of star regions. Star detection is a crucial step before star recognition, and its accuracy plays a crucial role in the successful recognition of subsequent stars. It also determines the accuracy of recognition and the effectiveness of attitude. Therefore, a fast and effective star detection method is crucial. The clustering and segmentation of star points are generally carried out using two methods, one is the global threshold method, and the other is the local threshold method, both of which can be collectively referred to as threshold segmentation methods. When using a star sensor to aim at the sky background for shooting, it can be observed that there is a significant difference in the grayscale of the stars and the background. In this case, empirical thresholds can be used to effectively separate the stars and the background.
However, when encountering the influence of stray light, the background in the star map is very bright due to the presence of stray light, even approaching the brightness values of some weak stars, or directly flooding the star points. Therefore, using a single global threshold segmentation method is not very suitable, and it is easy to overlook one aspect and miss the other, as it cannot ensure that the star points in each region are accurately detected. Therefore, it is necessary to use a local threshold segmentation method. Among them, the commonly used methods include background prediction algorithms. However, the local adaptive threshold method is limited by the complexity of computation, often resulting in slow computation speed and difficult implementation. Moreover, a simple local threshold method cannot be applicable to various working conditions and backgrounds.
At present, most detection algorithms applied on star sensor platforms do not have ideal suppression effects on stray light. Considering that the proportion of star points on the detector target surface is usually very small, about 2 × 2 to 7 × With a size of 7 pixels, star points can also be considered as small targets, and the idea of infrared weak target detection can be used to achieve star point detection. Small object detection is usually divided into two types: based on single frame images and based on video sequences. The first is a detection method based on filtering. It is also a common method used for small object detection in the early days. Filter based detection methods have the characteristic of simple operation and achieve background suppression by designing specific filters.
In addition, combining the idea of frequency domain operation, relevant filters are designed in the frequency domain to suppress noise by utilizing the difference in frequency functions between the target and other backgrounds and noise. Overall, although algorithms have low computational complexity, their detection performance is usually average and can only be applied to background suppression in certain scenarios, with low robustness.
Star point recognition algorithms can generally be divided into three categories:
(1) Star point recognition algorithm based on constellation features;
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.
(2) Star point recognition algorithm based on character pattern;
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.
(3) A star recognition algorithm based on intelligent behavior.
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.
Send us a message,we will answer your email shortly!
TY-Space Technology (Beijing) Ltd. is professional focusing on advanced attitude optical sensors, especially Star Trackers for space industry.
