A Star Identification Graph Algorithm Based on Angular Distance Matching Score Transfer

In order to detect gravitational waves and characterise their sources, three laser links were constructed with three identical satellites, such that interferometric measurements for scientific experiments can be carried out. The attitude of the spacecraft in the initial phase of laser link docking is provided by a star sensor (SSR) onboard the satellite. If the attitude measurement capacity of the SSR is improved, the efficiency of establishing laser linking can be elevated. An important technology for satellite attitude determination using SSRs is star identification. At present, a guide star catalogue (GSC) is the only basis for realising this. Hence, a method for improving the GSC, in terms of storage, completeness, and uniformity, is studied in this paper. First, the relationship between star numbers in the field of view (FOV) of a staring SSR, together with the noise equivalent angle (NEA) of the SSR—which determines the accuracy of the SSR—is discussed. Then, according to the relationship between the number of stars (NOS) in the FOV, the brightness of the stars, and the size of the FOV, two constraints are used to select stars in the SAO GSC. Finally, the performance of the GSCs generated by Decision Trees (DC), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), the Magnitude Filter Method (MFM), Gradient Boosting (GB), a Neural Network (NN), Random Forest (RF), and Stochastic Gradient Descent (SGD) is assessed. The results show that the GSC generated by the KNN method is better than those of other methods, in terms of storage, uniformity, and completeness. The KNN-generated GSC is suitable for high-accuracy spacecraft applications, such as gravitational detection satellites.

To endow a star sensor with strong robustness, low algorithm complexity, and a small database, this paper proposes an all-sky star identification algorithm based on spherical polar pattern matching. The proposed algorithm consists of three main steps. First, the guide star is rotated to be a polar star, and the polar and azimuth angles of neighboring stars are used as polar pattern elements of the guide star. Then, the relative azimuth histogram is applied to the spherical polar pattern matching, and a star pair after spherical polar pattern matching is identified through angular distance cross-verification. Finally, a reference star image is generated from the identified star pair to complete the matching process of all guide stars in the field of view. The proposed algorithm is verified by simulation experiments. The simulation results show that for a star sensor with a medium field of view (15° × 15°, 1024 × 1024 pixel) and a limiting magnitude of 6.0 Mv, the required database size is 161 KB. When false and missing star spots account for 50% of the guide stars and the star spot extraction error is 1.0 pixel, the average star identification time is 0.35 ms (@i7-4790), and the identification probability is 99.9%. However, when false and missing star spots account for 100% of the guide stars and the star spot extraction error is 5.0 pixel, the average star identification time is less than 2.0 ms, and the identification probability is 97.1%.

The reliability of star sensor in a harsh environment has recently become a research hotspot. In some harsh environment such as plume interference, a large number of false stars can be observed, leading to failure in star identification. In this paper, we propose a false star filtering algorithm, which can be used as a preprocessing algorithm for any existing star identification algorithm. By utilizing the difference between the motion of false stars and true stars, the algorithm performs angular distance tracking and star voting on multiple consecutive frames of star images and achieves false star filtering. The software simulation results show that for the star images containing more than 700 false stars, the algorithm is able to find out all true stars in less than 10 frames, and the success rate of the algorithm remains high when the star sensor rotates at up to 1°/s. The algorithm is also implemented on an existing star senor and evaluated with star images generated by a dynamic star simulator. The experimental results indicate that with the help of the proposed algorithm, the robustness of a normal star identification algorithm can be significantly improved.

Under dynamic conditions, motion blur is introduced to star images obtained by a star sensor. Motion blur affects the accuracy of the star centroid extraction and the identification of stars, further reducing the performance of the star sensor. In this paper, a star image restoration algorithm is investigated to reduce the effect of motion blur on the star image. The algorithm includes a blur kernel calculation aided by a MEMS gyroscope, blur kernel correction based on the structure of the star strip, and a star image reconstruction method based on scaled gradient projection (SGP). Firstly, the motion trajectory of the star spot is deduced, aided by a MEMS gyroscope. Moreover, the initial blur kernel is calculated by using the motion trajectory. Then, the structure information star strip is extracted by Delaunay triangulation. Based on the structure information, a blur kernel correction method is presented by utilizing the preconditioned conjugate gradient interior point algorithm to reduce the influence of bias and installation deviation of the gyroscope on the blur kernel. Furthermore, a speed-up image reconstruction method based on SGP is presented for time-saving. Simulated experiment results demonstrate that both the blur kernel determination and star image reconstruction methods are effective. A real star image experiment shows that the accuracy of the star centroid extraction and the number of identified stars increase after restoration by the proposed algorithm.

Star sensor needs real-time calibration to improve its navigation accuracy. Compared with other parameters, the position of the principal point is more easily affected by the measurement error, which leads to low calibration accuracy. Conventional star sensors on-orbit calibration methods mainly rely on the angular distance(AD) between stars. The observability of the principal point in this method and the calibration accuracy is not as well as that of other parameters. In this paper, an on-orbit calibration method of star sensor based on the angular distance subtraction(ADS) is proposed. The accuracy of the on-orbit calibration is improved by increasing the observability of the principal point in the calibration process, and the feasibility of this method is verified by the observability analysis. Finally, improved angular distance subtraction(IADS) models are proposed to solve the time-consuming problem of ADS method. In order to demonstrate the performance of the IADS models, simulation calibrations are conducted. The results show that the accuracy of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u₀</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v₀</i> of the IADS method is 62.7% and 15.9% higher than that of AD method when other parameters are set as nominal values. The calibration accuracy of the principal point is improved effectively.

The construction of a guide star catalog is crucial for a star sensor to achieve accurate star map recognition and attitude determination. At present, the methods of a guide star catalog for a large field of view (FOV) star sensor have been relatively mature. However, for a small-FOV star sensor, there are still certain problems, such as a large storage capacity of a guide star catalog, uneven distribution of stars, and easy occurrence of voids. To address these problems, we propose a construction method of a small-FOV star sensor guide star catalog based on the combination of the improved spherical spiral method (ISSM) and extreme learning machine (ELM) named the ISSM-ELM. First, a spiral reference point is used as an optical axis pointing of the star sensor, and the guide stars are preliminarily screened based on the star-diagonal distance between the star and the reference point, and the star-density and magnitude characteristics of the guide star. Then the ELM is used to supplement the guide star empty sky area to construct an integrity guide star catalog. The experimental results demonstrate that the proposed method can reduce the storage capacity of the guide star catalog, and improve its uniformity, integrity, and average brightness.

Star identification can be accomplished by several different available algorithms that identify the stars observed by a star tracker. However, efficiency and reliability remain key issues and the availability of new active pixel cameras requires new approaches. Two novel algorithms for recursive mode star identification are presented here. The first approach is derived by the spherical polygon search (SP-search) algorithm, it was used to access all the cataloged stars observed by the sensor field-of-view (FOV) and recursively add/remove candidate cataloged stars according to the predicted image motion induced by camera attitude dynamics. Star identification is then accomplished by a star pattern matching technique which identifies the observed stars in the reference catalog. The second method uses star neighborhood information and a catalog neighborhood pointer matrix to access the star catalog. In the recursive star identification process, and under the assumption of "slow" attitude dynamics, only the stars in the neighborhood of previously identified stars are considered for star identification in the succeeding frames. Numerical tests are performed to validate the absolute and relative efficiency of the proposed methods.

Observation angular distance error, as the difference between the actual observation angular distance and the reference angular distance, is an important parameter that affects the identification success rate, attitude measurement accuracy, and real-time performance of a terrestrial star tracker. It is the criterion to determine whether stars are identified in star identification but is still unclarified to date. To resolve the problem, the observation angular error model is presented in this work. This model determines the variation range of the observation angular distance error by analyzing the factors of astrometric transformations. Then, the optimal angular distance matching threshold expression for a terrestrial star tracker is presented on the basis of the proposed model for the optimal efficiency in star identification. Numerical simulations and a night sky experiment demonstrate that the differences between the theoretical model, simulation and actual experiment results are less than 0.5'' and thereby validate the reliability of our conclusions.

This paper focuses on the simulation and experimental testing for the star sensor system about position accuracy with different spectrums. The simulated model has been presented to analyze the effect of stellar spectrums on star sensor. Stellar spectrum types of K0, A, F are chosen as simples and simulated with software CODE V. The simulated results show that the RMS position error of the image spot is 0.24 pixels. An experiment is taken to verify this effect. By setting different band-pass light filters in the optical path structure, different spectrums of star light are achieved. The image spot centroids have been obtained at five different angles of view when the simulated star light is in different wavelength range. The experimental results show that the RMS position error is about 0.18 pixels. It is similar to the theoretical simulation analysis.

A novel method for subdivided locating of star image in star identification

A starlight refraction scheme with single star sensor used in autonomous satellite navigation system

The paper is devoted to the results of the star tracker 329K flight tests on board of the satellites Luch-5A and Luch-5B launched into geostationary orbit in December 2011 and November 2012 respec tively. Emphasis is placed on accuracy and photometric characteristics of the star tracker 329K. Keywords: star sensor, satellite Luch, satellite Loutch, guide star catalog, attitude control, Euler’s angles, inertial frame. 1. INTRODUCTION The 329K device, fabricated at JSC “NPP “Geofizika-Cosmos” (Moscow, Russia) is a wide-field star tracker with the FOV (field of view) of 22×17 degrees. A CCD matrix is used as the photo detector. The device’s sensitivity and optical characteristics guaranty that in 96% of cases at least four stars are pres ent in the device FOV. The star recognition is performed based on analysis of their mutual distances. Result of the device operation is output of orientation matrix, giving transition from the inertial frame (geocentric coordinates) to the body frame. The device overall view is presented in Fig. 1.

The larger area threshold scan window in star image is scanned with conventional star tracking algorithm under large maneuvering of the vehicle. This results that the number of calculating angular separation between two stars is increased. So the update rate of star sensor is decreased and the feasibility of errors star locations is increased. So, an autonomous predicted star locations algorithm with high angle velocity is presented in this paper. Firstly, the actual star locations are obtained within the threshold scan window of corresponding ideal star locations calculated by last frame in the current star image. Secondly the current attitude information is calculated with the obtaining actual star locations. Thirdly, the next ideal attitude is calculated with current attitude and last attitude. Finally, the next ideal star locations are calculated with the next ideal attitude. At last the algorithm are tested not only simulation but also at night sky experiment with the angular speed maneuvering of aircraft is 5.208681°/s in 2013. And the algorithm has be applied star sensor of satellite GNC.

This paper uses a radial basis function (RBF) neural network for star identification. A feature vector consisting of distance and angle cosine values is constructed as a library of recognition feature stars and input to the neural network. Then, the network’s output is the number of the corresponding primary star. Comparing the training efficiency and robustness of the grid algorithm with the BP neural network and radial basis function neural network for star identification, the experimental results show that the radial basis function neural network has a strong anti-interference ability against noise and pseudo-star interference. The following conclusions are drawn from the simulation tests: the sample recognition rate of radial basis function neural network algorithm can reach 96% for star position and star addition noise; when the proportion of pseudo-star reaches 20%, the sample recognition rate of radial basis function neural network algorithm is still higher than grid algorithm and BP neural network, which indicates that the radial basis function neural network algorithm has the solid anti-interference ability to both noise and pseudo-star. Therefore, the radial basis function neural network algorithm based on star Identification has specific application prospects.

Angular velocity is a critical parameter for satellites during on-orbit operation. Current gyro-free angular velocity estimation methods based on star sensors predominantly rely on multiple frames of star images and are effective under relatively small maneuvers of the star sensor. This paper proposes an angular velocity estimation approach utilizing single-frame star image data captured by a high-dynamic star sensor operating under rolling shutter exposure mode. By analyzing the motion patterns of star points in single-frame images, an angular velocity estimation optimization model was established with inter-star angular distances as the objective function. Accurate angular velocity estimation was achieved through the simulated annealing algorithm. To evaluate the star sensor's performance under high-dynamic conditions, three representative operational working conditions were simulated numerically. Experimental results demonstrate: under composite motion with dual-axis angular velocity of 15°/s and angular acceleration of 0.8°/s², the root mean square errors (RMSE) for triaxial angular velocity estimation are 0.0378°/s (X-axis), 0.0365°/s (Y-axis), and 0.1947°/s (Z-axis). Notably, when subjected to sinusoidal/cosinusoidal dynamic angular velocity excitation with amplitude of 15°/s, the maximum comprehensive error of triaxial angular velocity estimation does not exceed 0.103°/s, validating the algorithm's robustness in complex motion environments.