An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

Star identification is the foundation of star trackers, which are used to precisely determine the attitude of spacecraft. In this paper, we propose a novel star identification approach based on spectral graph matching. In the proposed approach, we construct a feature called the neighbor graph for each main star, transforming the star identification to the problem of finding the most similar neighbor graph. Then the rough search and graph matching are cooperated to form a dynamic search framework to solve the problem. In the rough search stage, the total edge weight in the minimum spanning tree of the neighbor graph is selected as an indicator, then the k-vector range search is applied for reducing the search scale. Spectral graph matching is utilized to achieve global matching, identifying all stars in the neighbor circle with good noise-tolerance ability. Extensive simulation experiments under the position noise, lost-star noise, and fake-star noise show that our approach achieves higher accuracy (mostly over 99%) and better robustness results compared with other baseline algorithms in most cases.

Design and Optimization of Star Recognition Algorithm Based on Hierarchical CNN

With the rapid-development of AI technology, artificial intelligence algorithms for the aerospace applications have shown very good simulation performance in many areas. Among the spaceborne application fields, star identification can be seen as a typical pattern recognition process. It’s also the key part of attitude determination of the satellites, which requires the algorithm to be robust and efficient due to the limited computing and storing resources of the spaceborne computers. Nevertheless, most of the previous algorithms are not possible to be applied in practical due to the reasons above. This article proposes a strategy of constructing ‘net-structure’ images of stars to build the datasets for training and testing. Besides, a hierarchical convolutional neural network(CNN) with a small size is also designed. It performs good results on robustness and efficiency in the experiments. In the end, a method of fusing the Conv layers and the batch normalization (BN)layers is also adopted to further accelerate the algorithm.

An Efficient and Robust Star Identification Algorithm Based on Neural Networks

A lost-in-space star identification algorithm based on a one-dimensional Convolutional Neural Network (1D CNN) is proposed. The lost-in-space star identification aims to identify stars observed with corresponding catalog stars when there is no prior attitude information. With the help of neural networks, the robustness and the speed of the star identification are improved greatly. In this paper, a modified log-Polar mapping is used to constructed rotation-invariant star patterns. Then a 1D CNN is utilized to classify the star patterns associated with guide stars. In the 1D CNN model, a global average pooling layer is used to replace fully-connected layers to reduce the number of parameters and the risk of overfitting. Experiments show that the proposed algorithm is highly robust to position noise, magnitude noise, and false stars. The identification accuracy is 98.1% with 5 pixels position noise, 97.4% with 5 false stars, and 97.7% with 0.5 Mv magnitude noise, respectively, which is significantly higher than the identification rate of the pyramid, optimized grid and modified log-polar algorithms. Moreover, the proposed algorithm guarantees a reliable star identification under dynamic conditions. The identification accuracy is 82.1% with angular velocity of 10 degrees per second. Furthermore, its identification time is as short as 32.7 miliseconds and the memory required is about 1920 kilobytes. The algorithm proposed is suitable for current embedded systems.

An End-to-End Identification Algorithm for Smearing Star Image

In the previous study, there were a few direct star identification (star-ID) algorithms for smearing star image. An end-to-end star-ID algorithm is proposed in this article, to directly identify the smearing image from star sensors with fast attitude maneuvering. Combined with convolutional neural networks and the self-attention mechanism of transformer encoder, the algorithm can effectively classify the smearing image and identify the star. Through feature extraction and position encoding, neural networks learn the position of stars to generate semantic information and realize the end-to-end identification for the smearing star image. The algorithm can also solve the problem of low identification rate due to smearing of long exposure time for images. A dataset of dynamic stars is analyzed and constructed based on multiple angular velocities. Experiment results show that, compared with representative algorithms, the identification rate of the proposed algorithm is improved at high angular velocities. When the three-axis angular velocity is 10°/s, the rate is still 60.4%. At the same time, the proposed algorithm has good robustness to position noise and magnitude noise.

YELLOW STAR IN THE PERCEPTION OF GERMAN JEWS (1941-1945)

На основании источников личного происхождения и официальных документов исследовано восприятие немецкими евреями введения опознавательного знака в виде жёлтой звезды в сентябре 1941 г. Установлено, что приверженцы сионистских идей носили знак с гордостью, подчёркивая своё безвинное мученичество. Для подавляющего большинства евреев ношение жёлтой звезды являлось сильным моральным ударом и в ретроспективе представлялось самой тяжёлой дискриминационной мерой за весь период нацистского господства. Часть их них отреагировала на введение жёлтой звезды, приняв быструю или медленную смерть, часть - пыталась в нарушение правительственного распоряжения скрывать её в общественных местах, а наиболее решительные избавлялись от неё на время или навсегда, уйдя в подполье. В послевоенных воспоминаниях евреев жёлтая звезда ассоциируется с унижениями и смертельной угрозой, а избавление от неё - с освобождением и надеждой на жизнь. Based on the sources of personal origin and official documents, the author investigates the perception of the yellow star identification mark introduction by German Jews in September 1941. It was established that the adherents of Zionist ideas wore the mark with pride, emphasizing their innocent martyrdom. For the overwhelming majority of Jews, wearing the yellow star was a strong moral blow and, in retrospect, seemed the most severe discriminatory measure in the entire period of Nazi rule. Some of them reacted to the introduction of the yellow star, accepting a quick or slow death, some tried to hide it in public places in violation of the government order, and the most decisive got rid of it for a while or forever, going underground. In the post-war memories of Jews, the yellow star is associated with humiliation and a mortal threat, and getting rid of it is associated with liberation and hope for life.

Star Identification Based on Multilayer Voting Algorithm for Star Sensors

This paper describes the multilayer voting algorithm, a novel autonomous star identification method for spacecraft attitude determination. The proposed algorithm includes two processes: an initial match process and a verification process. In the initial match process, a triangle voting scheme is used to acquire candidates of the detected stars, in which the triangle unit is adopted as the basic voting unit. During the identification process, feature extraction is implemented, and each triangle unit is described by its singular values. Then the singular values are used to search for candidates of the imaged triangle units, which further improve the efficiency and robustness of the algorithm. After the initial match step, a verification method is applied to eliminate incorrect candidates from the initial results and then outputting the final match results of the imaged stars. Experiments show that our algorithm has more robustness to position noise, magnitude noise, and false stars than the other three algorithms, the identification speed of our algorithm is largely faster than the geometric voting algorithm and optimized grid algorithm. However, it takes more memory, and SVD also seems faster.