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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 analytical model based on the match of angular distance is developed to evaluate the validity of different subgraphs. • The analytical model provides guidance to choose reliable and effective subgraphs forstar identification algorithms. • The effective and reliable simplest general subgraphs are chosen to achieve fast and direct star identification. [ABSTRACT FROM AUTHOR]

• An improved triangular-based star identification algorithm for low-cost star trackers is proposed. • The overall identification rate is improved when few stars are detected. • The proposed algorithm is highly robust to missing stars, and robust towards magnitude noise with an optimised star database. • The proposed algorithm is successfully implemented on a star tracker prototype based on ARM processor. • Our algorithm is more suitable for low-cost star trackers than previous algorithms. Star identification algorithms based on triangular-pattern are more suitable for low-cost star trackers since they require less star density in the field of view to operate effectively. In this paper, we propose a modified star pattern recognition algorithm based on the triangular-based algorithm of "LIEBE". The main contribution of the proposed work is twofold. First, a new strategy for the selection of star triplets is proposed for database construction. Second, new selection criteria of the reference star are considered for pattern generation process. A sky simulation program is developed to assess mainly the robustness against different conditions of noise. The obtained results show an improvement in the overall identification rate, more robustness towards missing stars, and more efficiency towards magnitude noise. Furthermore, our proposed algorithm shows comparable robustness with the recently proposed triangular algorithms despite their reliance on more accurate camera and a validation process. To assess the algorithm performance, the algorithm is implemented on a prototype of Data Processing Unit (DPU) based on ARM Cortex-M4 processor. In this part, we discuss the major design decisions and we present the hardware architecture of DPU. The algorithm shows promising running time at a reduced on-board database when implemented on ARM platform. [ABSTRACT FROM AUTHOR]

In the process of satellite attitude determination, satellites or sensors themselves often encounter a variety of turbulence influences due to the complexity of space environments. Such influences can lead to the mutation and non-Gaussian noises for the attitude determination system. To solve these problems, in this paper, we construct a unified error model for the turbulence influences, which is a non-Gaussian noise model, and propose an improved attitude filter method to restrict the turbulence noises and the system mutation to enhance attitude determination accuracy and robustness. The unified error model combined with jitters and vibrations in the actual process of satellite attitude determination is firstly designed. Then an Improved Adaptive Kalman filter (IAKF) based on both the Strong Tracking Filter (STF) and the Maximum Correntropy Filter (MCKF) is put forward. By using of the optimization principle with both of fading factor and Maximum Correntropy Criterion (MCC), this proposed filter algorithm can suppress the influences of system mutations and non-Gaussian noises at the same time. It can eliminate the system mutations and the turbulence errors, and achieve excellent robustness and the attitude determination accuracy for the nonlinear system. Extensive simulations of the proposed filter are conducted under the conditions of the Gaussian noises, system mutation with large outliers, non-Gaussian noise with turbulence noises, and both the mutation and non-Gaussian turbulence error. The results demonstrate that our filter outperforms the existing attitude filter algorithms significantly.

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. [ABSTRACT FROM AUTHOR]

Star trackers are autonomous, high-accuracy electronic systems used to determine the attitude of a spacecraft. In recent years, commercial-off-the-shelf (COTS)-based star trackers are growing in importance for low-cost and short-duration missions, but their fault tolerance against soft errors has not been studied in detail. In this paper, we propose a self-healing system protected with ad hoc techniques that can be used as the first step to implement a fault-tolerant COTS-based star tracker for smallsat applications. [ABSTRACT FROM AUTHOR]

Since star identification algorithms most likely yield multiple matching candidates, validation algorithms are required for star trackers. Traditional validation is performed in an iteratively cascaded way by incrementally introducing new star points for help, costing plenty of inefficient lookups and complex combinations, but leading to nondeterministic results only. Focusing on this problem, this paper considers a novel validation idea that the attitude estimate perturbation caused by bounded measuring noises should be bounded as well. As a result, a two-step validation algorithm is proposed following a noniterative deterministic structure. Theoretical analyses and simulation examples are both given to demonstrate the algorithm. [ABSTRACT FROM AUTHOR]

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 ∘ × 13.8 ∘ , similar to the ASTRO 15 sensor manufactured by Jena-Optronik GmbH company. [ABSTRACT FROM AUTHOR]

The star tracker is widely used for high-accuracy missions due to its high accuracy position high autonomy and low power consumption. On the other hand, the ability of interference suppression of the star tracker has always been a hot issue of concern. A SLIC-DBSCAN-based algorithm for extracting effective information from a single image with strong interference has been developed in this paper to remove interferences. Firstly, the restricted LC (luminance-based contrast) transformation is utilized to enhance the contrast between background noise and the large-area interference. Then, SLIC (the simple linear iterative clustering) algorithm is adopted to segment the saliency map and in this process, optimized parameters are harnessed. Finally, from these segments, features are extracted and superpixels with similar features are combined by using DBSCAN (density-based spatial clustering of applications with noise). The proposed algorithm is proved effective by successfully removing large-area interference and extracting star spots from the sky region of the real star image.

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. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of star identification in the presence of high slew rates, false objects and image deformations introduced by the rolling shutter. These problems can affect the operating life of star trackers and worsen the nominal performances. The proposed methodology relies on a technique named Improved Multi-Poles Algorithm, especially designed for robustness to false objects and slew rates. Angular velocities up to five degrees per second are considered so that stars are seen no more as near-circular spots but appear as streaks. The image deformation due to the rolling shutter of modern active pixel sensor detectors is compensated by means of a mathematical model based on a first order approximation of problem. A star tracker high fidelity simulator generates the input images considering typical noises due to the electronics and space environment. The reported results show that the proposed approach guarantees a reliable star identification and attitude determination with angular velocity from zero to five degrees per second. • Star identification performed with slew rates, false objects and APS deformation. • Robustness to false objects guaranteed by the Improved Multi-Poles Algorithm. • A rolling shutter compensation model is proposed to consider image deformation. • The results are obtained using a high-fidelity star tracker simulator. • Reliable star identification guaranteed with angular rate from 0 to 5 deg/sec. [ABSTRACT FROM AUTHOR]

Refinement of the electrostatic gyroscope (ESG) drift model aboard an orbital maneuverable satellite for remote sensing of the Earth's surface is considered. Some changes of the drift model also hold for the ESG ground-based applications. The paper also analyzes the quasi-continuous correction mode of the ESG-based strapdown inertial attitude reference system (SIARS) using raw (unsmoothed) data from the star tracker and the extended Kalman filter (EKF) algorithm. A special feature of solving the problem of SIARS output data correction and the calibration problem in this mode is rejection of incorrect measurements. The algorithm for this problem solution is described. The ESG drift model is given both for the SIARS calibration mode in order to ensure an adequate prediction of ESG drifts over a long time interval and for the quasi-continuous correction mode under spacecraft maneuvering conditions. The results of data processing of the flight tests of the SIARS and the star tracker aboard one of the spacecraft are discussed. [ABSTRACT FROM AUTHOR]

Abstract 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. [ABSTRACT FROM AUTHOR]

A novel star pattern recognition technique for a “Lost-in-space” mode star tracker is presented in this paper. First, the two-dimensional (2-D) vectors connecting the stars are constructed in a rotation-invariant frame. Later, the additive property of 2-D vectors is integrated with the rotation-invariant frame to build a vector sequence for star identification. The proposed technique achieves an identification accuracy of 98.7% and has a run-time of only 12 ms for real-time testing on star images. [ABSTRACT FROM AUTHOR]

For all common satellite attitude determination sensors, star trackers provide the most accurate measurement. However, these devices can be both large and expensive, and for some CubeSat applications it would not be suitable. Star trackers have in the past been successfully made for CubeSats. This paper investigates star tracker algorithms, implemented with a smartphone, so it may be used for testing attitude determination on a CubeSat. By testing through a proposed implementation, star centroids should be found by the moment method, stars should be identified by planar triangles, and QUEST should be used for attitude estimation. Smeared star images should be avoided and blurred images provide greater accuracy. Using these techniques, a star tracker using a smartphone may be constructed for attitude determination testing and software development, applied in the lost-in-space situation. This may be applied to QKD CubeSats, which require an attitude precision below 0.01°. [ABSTRACT FROM AUTHOR]

A high update rate is always one of the vital indices of star trackers. By recording star positions at N moments within a single star image, the multi-exposure imaging approach (MEIA) proposed in an earlier study can improve the attitude update rate of star trackers by N times. Unfortunately, when the existing star matching method is adopted to match the observed and predicted stars in MEIA, the matching time is significantly increased with the increase in multi-exposure times, N, or the number of navigation stars, M, which sharply affects the MEIA’s performance. Therefore, a fast star matching method based on double K-vector lookup tables (DKVLUTs) is proposed to address the above issue. In this method, the information of all predicted stars is used to establish the DKVLUT, and thus, the speed of the whole matching process between observed and predicted stars would be increased effectively by means of the DKVLUT. Both simulations and experiments are conducted to verify the performance of the proposed method. The results both show that the matching time of the proposed method is reduced by nearly one order of magnitude compared with that of the existing method, which demonstrates the feasibility and effectiveness of the proposed method.

The attitude vibration of a carrier has a significant effect on the installed star tracker. However, this effect has not been sufficiently studied. In this paper, the attitude model of a star tracker is investigated under complex vibration conditions. First, a star spot motion model applicable to complex angular motion is constructed. Then, the linearization conditions of the model are presented. Based on the linearized model, sinusoidal and random vibrations are analyzed, and an attitude model is established. The proposed attitude model provides the modulation factor of the response to sinusoidal vibrations and the power transfer function of the response to random vibrations and also elaborates on the synthesis mechanism between the responses to a complex vibration and its components. Simulations and ground experiments were conducted, and the results show that the maximum relative error was 6.66% and 3.82% for sinusoidal and random vibrations, respectively. • An attitude model of star trackers is established under complex vibration conditions. • Universal linearization conditions for star spot motions are presented. • The response of a star tracker to random vibrations is analyzed. • A fast steering mirror is used to perform vibration tests on star trackers. [ABSTRACT FROM AUTHOR]

Attitude Determining is one of the major and critical satellites space missions. In this study, a new method to Attitude determination of satellites is presented. Such that, based on the proposed method search space will be more limited then accuracy and speed of attitude determination in the proposed method has risen. At first in this method, implementation and the test algorithms will be discussed, after these some algorithms, such as navigation, pattern recognition and ultimately attitude determination will be reviewed. In order to implement these algorithm. High quality images of stars which must provided by the star tracker camera requires to implement. Really these images to perform the necessary processing sent to the processor so the processor based on designed algorithms, determines the attitude of camera and satellite in all three axes. This means that some features considered for star tracker and based on them begins the designing process. The range of accurately determination for star tracker is one of these features. In this article, the ranges of two axes of Yao and Pitch less than 20 seconds on the scale of degree are considered and in the roll axis less than 100 seconds is intended. Can show in the results, much better accuracy and less than initial assumptions have been achieved. It also carried out by an adaptive identified algorithm so that the brighter stars are identified and based on their attitude determination, the sensor accuracy have increased. Because of according research, the clearer stars, have more accurate in calculation. The other important feature is the speed of attitude detection which performed by 1 GHz processor, and correct identification of pyramidal algorithm where have reached less than 15 milliseconds. Due to the duration, the desire update rate gained. Other important parameters which influence the accuracy of the attitude determination is knowing the exact coordinates of the intersection point vector of focal length lens with image sensors. By Land calibration for camera with a good accuracy, these parameters were estimated.

Automatic control of satellites and spacecrafts, has been extensively paid attention. Attitude determination is one of the most important procedures to control a spacecraft. Star trackers are widely used for attitude determining. A star tracker provides images from the around space and try to identify the stars in the images. Several algorithms are proposed to this end. However, most of these algorithms use the raw measurements to star identification and attitude determination. As the measurements are often affected by various types of noise, the performance of such algorithms is degraded. Here, we employ tracking algorithms to improve the performance of existing methods for attitude determining. The Kalman filter-based tracking algorithms are shown to have satisfactory results for object tracking. We use the JPDAF to build an algorithm for accurate tracking of stars locations in successive images and, consequently, determining the attitude of spacecraft. The presented algorithm is compared with the well known algorithm for attitude determining called SNA.

Developing star trackers quickly is non-trivial. Achieving reproducible results and comparing different algorithms are also open problems. In this sense, this work proposes the use of synthetic star images (a simulated sky), allied with the standardized structure of the Universal Verification Methodology as the base of a design approach. The aim is to organize the project, speed up the development time by providing a standard verification methodology. Future rework is reduced through two methods: a verification platform that us shared under a free software licence; and the layout of Universal Verification Methodology enforces reusability of code through an object-oriented approach. We propose a black-box structure for the verification platform with standard interfaces, and provide examples showing how this approach can be applied to the development of a star tracker for small satellites, targeting a system-on-a-chip design. The same test benches were applied to both early conceptual software-only implementations, and later optimized software-hardware hybrid systems, in a hardware-in-the-loop configuration. This test bench reuse strategy was interesting also to show the regression test capability of the developed platform. Furthermore, the simulator was used to inject specific noise, in order to evaluate the system under some real-world conditions.

Star trackers are navigation sensors that are used for attitude determination of a satellite relative to certain stars. A star tracker is required to be accurate and also consume as little power as possible in order to be used in small satellites. While traditional approaches use lookup tables for identifying stars, the latest advances in star tracking use neural networks for automatic star identification. This manuscript evaluates two low-cost processors capable of running a star identification neural network, the Intel Movidius Myriad 2 Vision Processing Unit (VPU) and the STM32 Microcontroller. The intention of this manuscript is to compare the accuracy and power usage to evaluate the suitability of each device for use in a star tracker. The Myriad 2 VPU and the STM32 Microcontroller have been specifically chosen because of their performance on computer vision algorithms alongside being cost-effective and low power consuming devices. The experimental results showed that the Myriad 2 proved to be efficient and consumed around 1 Watt of power while maintaining 99.08% accuracy with an input including false stars. Comparatively the STM32 was able to deliver comparable accuracy (99.07%) and power measurement results. The proposed experimental setup is beneficial for small spacecraft missions that require low-cost and low power consuming star trackers.

Star Trackers are often the most accurate instrument in an Attitude Determination and Control Systems, but often present a slow update rate, requiring additional sensor and sensor fusion algorithms to provide a smoother and faster output. However, the available rate gyros are either noisy, or expensive and heavy. The proposed work investigates the feasibility of high-speed star trackers with modern optics, sensors, and computing systems. Firstly, we investigate the sensitivity of an optoelectrical acquisition system stimulated by dim stars, secondly, we propose and evaluate an algorithm designed to operate at high speed and to be compatible with an Field-Programmable Gate Array implementation, before evaluating the performance of the implementation on FPGA. Finally, we debate the usability of such a system, both in terms of compatibility with a mission and CubeSat ecosystems, and in terms of performance. As a result, aside from removing the need for a rate gyro, Attitude Determination and Control Systems overall pointing performances can be increased. The proposed attitude determination system achieved a 0.001° accuracy, with a 99.1% sky coverage and an ability to reject false-positive while performing a single-frame lost-in-space star identification at a 50 Hz update rate with a total delay of 19 ms, including 13 ms.