False Stars Research Articles

A Robust Star Identification Algorithm for Resident Space Object Surveillance

Star identification algorithms can be applied to resident space object (RSO) surveillance, which includes a large number of stars and false stars. This paper proposes an efficient, robust star identification algorithm for RSO surveillance based on a neural network. First, a feature called equal-frequency binning radial feature (EFB-RF) is proposed for guide stars, and a superficial neural network is constructed for feature classification. Then the training set is generated based on EFB-RF. Finally, the remaining stars are identified using a residual star matching method. The simulation experiment and results show that the identification rate of our algorithm can reach 99.82% under 1 pixel position noise, and it can reach 99.54% under 5% false stars. When the percentage of missing stars is 15%, it can reach 99.40%. The algorithm is verified by RSO surveillance.

A new star identification using patterns in the form of Gaussian mixture models

This paper presents an innovative star identification algorithm specifically designed for lost-in-space scenarios in satellite missions. The algorithm introduces a unique approach by utilizing singular values to generate Gaussian mixture models as distinctive features for star identification. By employing a matching process involving candidate filtering, similarity comparison, and validation, the algorithm demonstrates remarkable performance in precisely identifying stars while minimizing false identifications even in demanding environmental conditions. Extensive simulations are conducted to evaluate the algorithm’s effectiveness under various scenarios with positional error and the presence of false stars. The algorithm exhibits reliable and consistent performance, making it highly suitable for dynamic scenarios and contributing to the reduction of hardware burden in star sensor systems. Furthermore, the proposed algorithm offers prominent performance across varying field of view sizes, thereby enhancing its practicality and usability in a wide range of missions.

A lost-in-space star identification algorithm based on regularized pattern recognition

Accuracy in spacecraft attitude determination has been improved continuously in parallel to scientific payloads requiring higher accuracy. Star trackers aim to maintain attitude determination by matching observations to the reference stars. They provide very high accuracy at any point in space at cost of higher computational complexity. This study presents an algorithm of lost-in-space star identification with high accuracy and acceptable computational complexity. This task is achieved by means of a novel dictionary-based star matching method by applying nearest neighbor classification through a binary search of Euclidean distances and regularization sequentially, accompanied by use of unconventional feature vectors representing each observation individually. The proposed method yields an estimated direction of the boresight vector orthogonal to the camera plane with an estimated axial rotation. The Hipparcos catalog is used for database generation, and the sensor parameters are taken from the project SharjahSat-1. The performance is evaluated in consideration of noise including position, brightness and false stars. By means of complexity analysis of database size and average run time, it is shown that the proposed method achieves better accuracy than other methods and maintains very high robustness to position noise and brightness noise. A high level of accuracy is achieved with an amount of database size and average run time that are competitive in the recent literature. It is demonstrated that the levels of pointing accuracy offered by state-of-the-art ADCS models are achieved by the proposed method with the given probabilities.

A Practical Star Image Registration Algorithm Using Radial Module and Rotation Angle Features

Star image registration is the most important step in the application of astronomical image differencing, stacking, and mosaicking, which requires high robustness, accuracy, and real-time capability on the part of the algorithm. At present, there are no high-performance registration algorithms available in this field. In the present paper, we propose a star image registration algorithm that relies only on radial module features (RMF) and rotation angle features (RAF) while providing excellent robustness, high accuracy, and good real-time performance. The test results on a large amount of simulated and real data show that the comprehensive performance of the proposed algorithm is significantly better than the four classical baseline algorithms as judged by the presence of rotation, insufficient overlapping area, false stars, position deviation, magnitude deviation, and complex sky background, making it a more ideal star image registration algorithm than current alternatives.

Star-Identification System Based on Polygon Recognition

Accurate attitude determination is crucial for satellites and spacecraft. Among attitude determination devices, star sensors are the most accurate. Solving the lost-in-space problem is the most critical function of the star sensor. Our research introduces a novel star-identification system that utilizes a polygon-recognition algorithm to assign a unique complex number to polygons created by stars. This system aims to solve the lost-in-space problem. Our system includes a full solution with a lens, image sensor, processing unit, and algorithm implementation. To test the system’s performance, we analyzed 100 night sky images that resembled what a real star sensor in orbit would experience. We used a k-d tree algorithm to accelerate the search in the star catalog of complex numbers. We implemented various verification methods, including internal polygon verification and a voting mechanism, to ensure the system’s reliability. We obtained the star database used as a reference from the Gaia DR2 catalog, which we filtered, to eliminate irrelevant stars, and which we arranged by apparent magnitude. Despite manually introducing up to three false stars, the system successfully identified at least one star in 97% of the analyzed images.

An Autonomous Global Star Identification Algorithm Based on the Fast MST Index and Robust Multi-Order CCA Pattern

Star identification plays a key role in spacecraft attitude measurement. Currently, most star identification algorithms tend to perform well only in a scene without noise and are highly sensitive to noise. To solve this problem, this paper proposes a star identification algorithm based on the maximum spanning tree (MST) index and multi-order continuous cycle angle (CCA) intended for the lost-in-space mode. In addition, a neighboring star selection method named dynamic eight-quadrant (DEQ) is developed. First, the DEQ method is used to select high-confidence neighboring stars for the main star. Then, the star image is regarded as a graph, and the Prim algorithm is employed to construct the MST pattern for each guide star, which is then combined with the K vector index to perform the main star candidate search. Finally, the Jackard similarity voting for the multi-order CCA of the main star is used to identify the main star, and the global neighboring star identification is conducted by the multi-order CCA of neighboring stars. The simulated and real star images test results show that compared with five mainstream algorithms, when the position noise is 1 pixel, the number of false stars is five, the magnitude noise is 0.5, and the identification accuracy of the proposed algorithm is higher than 98.5%. Therefore, the proposed algorithm has excellent anti-noise ability in comparison to other algorithms.

Proton Radiation Effects of CMOS Image Sensors on Different Star Map Recognition Algorithms for Star Sensors

Star sensors are widely used by satellites for their precise pointing accuracy. However, protons in space will cause cumulative effects and single-event transients in the imaging systems of star sensors. These effects will affect the success rate of star map recognition of star sensors. In this paper, proton irradiation experiments and field tests were carried out in turn, and three typical star recognition algorithms were used to recognize the star maps. The results showed that cumulative effects led to a decrease in the number of identifiable stars, which greatly affected the recognition success rate of the grid algorithm. Hot pixels caused by displacement damage effects increased the star centroid positioning error, leading to a decrease in the recognition success rate of the triangle algorithm and pyramid algorithm. Single-event transients produced by protons hitting the image sensor are similar to the grayscale value and shape of a star, and were recognized as “false stars”, which had a significant impact on the success rate of the three recognition algorithms. In general, the pyramid algorithm was more effective than the other two algorithms in identifying the affected star map, and the recognition success rate of the grid algorithm was significantly reduced.

A Star-Identification Algorithm Based on Global Multi-Triangle Voting

A star-identification algorithm aimed at identifying imaged stars in a “lost in space” scene, named the global multi-triangle voting algorithm (GMTV), is presented in this paper. There are two core parts included in the proposed algorithm: in the initial match part, triangle feature units are treated as vote units to find the initial match relationship via matching vote units and counting the vote number of each catalog star. During this step, the principal component analysis (PCA) method is implemented to reduce feature dimensions, and a two-dimension lookup table and fuzzy match strategy are utilized to promote database searching efficiency and noise tolerance. After acquiring the initial match results, a verification part is implemented to filter potential errors from initial candidates by the largest cluster method and output the final identification results. The proposed algorithm achieves a 98.6% identification rate with 2.0 pixels position noise and exhibits more robustness to position noise, magnitude noise, and false stars of different levels than the two reference algorithms used in simulations. In addition, our algorithm’s real-time performance is better than reference algorithms, but it requires a larger database.

A Robust Star Identification Algorithm Based on a Masked Distance Map

The authors of this paper propose a robust star identification algorithm for a ‘Lost-In-Space’-mode star tracker for lost-cost CubeSat missions. A two-step identification framework and an embedded validation mechanism were designed to accelerate the process. In the first step, a masked distance map is designed to provide a shortlist of stars, and the embedded fast validation process enables the direct output of validated stars before the second step. In the second step, local similarity is utilized to select a set of stars from those shortlisted, and the final validation procedure rejects all unsatisfactory stars. This algorithm can provide reliable and robust recognition even when the captured star images include severe star positioning errors, missing stars and false stars. The proposed algorithm was verified by a simulation study under various conditions. As low-cost star sensors face harsh and unknown environments during deep space CubeSat missions such as asteroid exploration, the proposed algorithm with high robustness will provide an important function.

A Star Identification Algorithm Based on Recommended Radial Pattern

Star identification is a key technology in the research of star sensors. As a classical star pattern for identification, the radial pattern of a chosen star represents the geometric distribution of neighboring stars. Previous researches usually combine the rotation-invariant radial pattern with other less reliable patterns like cyclic pattern to identify the chosen star. Seldom studies use the radial pattern for direct identification as it is sensitive to noise and inefficient to eliminate spurious matches. To solve the problem, a novel star identification algorithm based on the recommended radial pattern was developed. The algorithm introduced the Apriori algorithm to mine the association rules between a star and neighboring stars, recommend the companion stars, and generate the recommended radial pattern. Based on the association rules, the algorithm integrated the matching results of a star and recommended companion stars to identify the chosen star. The simulation test and night sky image test both show that the proposed algorithm is robust to position noise, brightness noise, and false stars. The novel matching strategy is not limited to the radial pattern algorithms, it provides a new idea for all the star pattern algorithms.

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 with 5 pixels position noise, with 5 false stars, and 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 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.

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.

A star identification algorithm based on simplest general subgraph

Subgraph isomorphism-based star identification algorithms require fewer stars than pattern-based algorithms and are suitable for practical application. Polygon algorithms and match group algorithms, as two typical subgraph isomorphism-based algorithms, both have disadvantages in efficiency and reliability. A novel star identification algorithm is presented in this study to solve this problem. We develop an analytical model to evaluate the validity of different subgraphs, which provides guidance to choose subgraphs. Based on the model, a series of effective and reliable subgraphs with different numbers of vertices, defined as the simplest general subgraphs, are chosen to achieve fast and direct star identification. The star matching strategy is divided into two basic steps. Based on the voting strategy, a star is initially identified by building match groups. It's further identified by building the simplest general subgraphs determined by the size of match groups. A verification approach of reprojection is adopted to improve the robustness of the algorithm. Compared with similar algorithms, the simulation test and night sky image test both show that the proposed algorithm is more robust to position noise, brightness noise, and false stars.

An artificial intelligence enhanced star identification algorithm

An artificial intelligence enhanced star identification algorithm is proposed for star trackers in lost-in-space mode. A convolutional neural network model based on Vgg16 is used in the artificial intelligence algorithm to classify star images. The training dataset is constructed to achieve the networks’ optimal performance. Simulation results show that the proposed algorithm is highly robust to many kinds of noise, including position noise, magnitude noise, false stars, and the tracker’s angular velocity. With a deep convolutional neural network, the identification accuracy is maintained at 96% despite noise and interruptions, which is a significant improvement to traditional pyramid and grid algorithms.

Automatic removal of false image stars in disk-resolved images of the Cassini Imaging Science Subsystem

Taking a large number of images, the Cassini Imaging Science Subsystem(ISS) has been routinely used in astrometry. In ISS images, disk-resolved objects often lead to false detection of stars that disturb the camera pointing correction. The aimof this study was to develop an automated processingmethod to remove the false image stars in disk-resolved objects in ISS images. The method included the following steps: extracting edges, segmenting boundary arcs, fitting circles and excluding false image stars. The proposed method was tested using 200 ISS images. Preliminary experimental results show that it can remove the false image stars in more than 95% of ISS images with disk-resolved objects in a fully automatic manner, i.e., outperforming the traditional circle detection based on Circular Hough Transform (CHT) by 17%. In addition, its speed is more than twice as fast as that of the CHT method. It is also more robust (no manual parameter tuning is needed) when compared with CHT. The proposed method was also applied to a set of ISS images of Rhea to eliminate the mismatch in pointing correction in automatic procedure. Experiment results showed that the precision of final astrometry results can be improve by roughly 2 times that of automatic procedure without the method. It proved that the proposed method is helpful in the astrometry of ISS images in a fully automatic manner.

A comparative analysis of star identification algorithms

This paper aims to examine different star identification algorithms in star sensors and compare the previous algorithms with the newly proposed algorithms. The star identification algorithms in this paper include three geometric methods, which require a minimum of five stars in the sensor field of view. These algorithms are compared with the angle, combined triangle, and pyramid algorithms in terms of speed, accuracy, storage capacity, and resistance to false stars using MATLAB software. Except for the angle algorithm, all the other star identification algorithms have high precision and resistance to false stars. In addition, they have a lower speed than those of the algorithms that use fewer stars to form the pattern. Considering the number of stars required for the algorithm, the newly proposed star identification algorithms are suitable for the second generation of star sensors, which have a larger field of view. The simulations are related to the stars brighter than magnitude 4, as well as a star sensor with a square-shaped field of view with a dimension of $13.8^{\circ }\times 13.8^{\circ }$, similar to the ASTRO 15 sensor manufactured by Jena-Optronik GmbH company.

Star Identification Based on Spider-Web Image and Hierarchical CNN

Unlike the traditional star pattern recognition algorithms that extract star features as a vector to be matched in a database, in this article, a spider-web image is constructed and a hierarchical convolution neural network (CNN) model is proposed to recognize this spider-web image. Stars are linked with different color lines based on the angle distance to enhance the discrimination of the spider-web images. A training dataset and testing dataset are constructed based on spider-web images for CNN model training. A hierarchical CNN model with two stages, which are designed to perform first step identification and recognize similar spider-web images, respectively, is proposed. Experiment results show that, compared with other algorithms, the proposed algorithm is more robust toward the position noise, magnitude noise, small numbers of stars in the field of view, and the presence of false stars.

Algorithm with Patterned Singular Value Approach for Highly Reliable Autonomous Star Identification.

In the work reported in this paper, a lost-in-space star pattern identification algorithm for agile spacecraft was studied. Generally, the operation of a star tracker is known to exhibit serious degradation or even failure during fast attitude maneuvers. While tracking methods are widely used solutions to handle the dynamic conditions, they require prior information about the initial orientation. Therefore, the tracking methods may not be adequate for autonomy of attitude and control systems. In this paper a novel autonomous identification method for dynamic conditions is proposed. Additional constraints are taken into account that can significantly decrease the number of stars imaged and the centroid accuracy. A strategy combining two existing classes for star pattern identification is proposed. The new approach is intended to provide a unique way to determine the identity of stars that promises robustness against noise and rapid identification. Moreover, representative algorithms implemented in actual space applications were utilized as counterparts to analyze the performance of the proposed method in various scenarios. Numerical simulations show that the proposed method is not only highly robust against positional noise and false stars, but also guarantees fast run-time, which is appropriate for high-speed applications.

A star identification algorithm based on radial and dynamic cyclic features of star pattern

A full-sky star identification algorithm based on radial and dynamic cyclic patterns is presented with the aim of solving the “lost-in-space” problem. The dynamic cyclic pattern match is applied with a maximum cumulate comparison method to identify sensor-catalog pairings in initial match, which substantially eliminates the effects of the star position noise, magnitude noise, and false stars. After initial match pairings of stars are obtained, a chain part extension technique is employed to quickly search for the longest match chain as the final result. Experimental results indicate that the proposed algorithm is highly robust to star position noise, magnitude noise and false stars. In a series of simulations, the identification rate of the algorithm is 97.50% with 2.0 pixels star position noise, 96.90% with 0.4 Mv star magnitude noise and 95.30% with four false stars respectively. Moreover, the algorithm achieves an identification rate of 58.08% when only six stars are in the field of view.

False Star Filtering for Star Sensor Based on Angular Distance Tracking

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.