Recursive mode star identification algorithms

Star trackers are currently the most accurate spacecraft attitude sensors. As a result, they are widely used in remote sensing satellites. Since traditional charge-coupled device (CCD)-based star trackers have a limited sensitivity range and dynamic range, the matching process for a star tracker is typically not very sensitive to star brightness. For active pixel sensor (APS) star trackers, the intensity of an imaged star is valuable information that can be used in star identification process. In this paper an improved brightness referenced star identification algorithm is presented. This algorithm utilizes the k-vector search theory and adds imaged stars' intensities to narrow the search scope and therefore increase the efficiency of the matching process. Based on different imaging conditions (slew, bright bodies, etc.) the developed matching algorithm operates in one of two identification modes: a three-star mode, and a four-star mode. If the reference bright stars (the stars brighter than three magnitude) show up, the algorithm runs the three-star mode and efficiency is further improved. The proposed method was compared with other two distinctive methods the pyramid and geometric voting methods. All three methods were tested with simulation data and actual in orbit data from the APS star tracker of ZY-3. Using a catalog composed of 1500 stars, the results show that without false stars the efficiency of this new method is 4∼5 times that of the pyramid method and 35∼37 times that of the geometric method.

In this paper, a laboratory model is designed to evaluate the performance of star tracker algorithms for attitude determination of a satellite. Star tracker is the most accurate attitude sensor that determines satellite direction by applying centroiding algorithm, star identification and attitude determination. To utilize such algorithms, first, high quality of star images are needed which should be provided through the star tracker camera. Then, such images are given to processors and it determines the attitude of camera and satellite in three axes based on mentioned algorithms. First, in preliminary design, we define important star tracker parameters, like accuracy, detector, processor and field of view. In this paper, we have considered the range accuracy in yaw/pitch axis less than 20 arcsec and for roll axis less than 100 arcsec. To improve the attitude determination accuracy, we have applied an adaptive structure in centroiding algorithm, as only brightness stars are selected and identification algorithm is done based on them. Another important parameter is speed of identification algorithm which is handled by an ARM processor and improving pyramid algorithm, we reached less than 25 ms. Duo to this time, updating rate would be desired. Also knowing the coordination of bore sight direction that is distance between focal lens and imager is another important parameter that affects the accuracy of attitude and by ground calibration of camera, this parameter could be estimated carefully. Finally, implementation results on real images that are captured by a star tracker demonstrate optimum performance of algorithms.

Star identification robust to angular rates and false objects with rolling shutter compensation

After the successfully launched hypersonic SHarp Edge Flight EXperiment (SHEFEX) in 2005, DLR has scheduled a second re-entry experiment SHEFEX 2 for 2010. The novel flight control system based on steerable canard fins requires a precise knowledge of the inertial spacecraft attitude. For this purpose, a star tracker is to be conceived and integrated in an inertial navigation system, updating the attitude information before re-entry. The developed star tracker is considered to be a low cost and low accuracy sensor that is suitable for the proposed mission and the attitude accuracy requirements. This sensor is based on an off-the-shelf camera and a PC104 computer communicating with the navigation computer. The attitude determination software consists of a processing chain including the camera control, the image processing, the star identification and the attitude estimation. The emphasis of the work is on developing a simple but robust system. Therefore, different algorithm concepts for each element of the chain have been implemented, tested and compared. Furthermore, star image simulation software has been developed, allowing performance and speed tests of the single blocks and the processing chain as a unit. The paper presents the star tracker system, details some newly developed parts and discusses the test results.

Italian Space Agency has funded, as one of the possible Italian experiments on board the International Space Station, the development of a new star tracker, called StarNav III, which observes the sky by means of three orthogonal fields of view and focuses all the star images onto a single CCD/CMOS imager. The main characteristics of this sensor are a high gain in the attitude accuracy estimation and in the operating time. These benefits cost, however, in terms of hardware and software design which are, in turn, more complex. The completed StarNav III feasibility study has output a variety of new software approaches (as, for instance, for acquisition, centroiding, star identification, and mirror misalignment), most of which can directly be applied to almost all of the existing CCD/CMOS star trackers. This paper shows the principal aspects of the relevant feasibility study.

The successful identification of stars is a fundamental prerequisite for satellite attitude determination by star sensors. Conventional star identification algorithms typically construct specific subgraphs using a set of bright stars or generate patterns based on angular distances between stars and their neighboring stars. However, these methods often fail when insufficient detectable stars or bright noncooperative space objects are in the field of view (FOV). Seldom studies use angular distance matching among all stars for direct identification as it is complex to calculate and store. To solve the problem, we proposed a graph algorithm based on angular distance matching score transfer, which utilizes a graph data structure, where nodes store angular distance matching scores between stars and transfer scores through graph edge weights, overcoming the limitations of incomplete utilization of angular distance matching results. We also established a math model for edge weights based on the probabilities of star pair occurrences. Simulation tests and night sky image experiments demonstrate the robustness of this algorithm against position errors, brightness errors, and fake stars. By equally using information from all sensor stars, even in cases with multiple missing stars, numerous fake stars, and extremely bright interfering stars, the identification rate remains consistently above 99.53%. This algorithm represents a highly robust star identification method and lays the foundation for future research in intelligent sensing of space environmental objects using star sensors.

Grid algorithm is a classical star identification algorithm based on star pattern. A three-dimensional grid algorithm for all-sky autonomous star identification is proposed, which is associated with the information of star view magnitude. In contrast with traditional grid algorithm that constructs the grid cells on two-dimensional plane (e.g. x-y coordinate plane), the proposed approach makes use of the star view magnitudes of the neighboring stars as the third dimension (e.g. z-axis). A pattern is generated for each of its three-dimensional grid cells that contain neighboring star are 1, and those without are 0. The progress of star identification is to determine which pattern in the database is associated with the particular sensor pattern. Simulation shows that this method can achieve identification rate of 98.0% while the standard deviation of star position error and star view magnitudes are 1 pixel and 0.3Mv respectively. Compared with the traditional grid algorithm, the identification rate is higher, and the average runtime is 50 percent shorter.

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.

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.

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 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.

The concept of software-defined satellite (SDS) enables in-orbit software reconfiguration and payload virtualization, significantly enhancing the configurability and cross-mission reusability of imaging payloads. However, this flexibility also exposes traditional star identification methods-which depend on fixed intrinsic camera parameters-to severe robustness limitations when dealing with multiple cameras and varying operational conditions. Therefore, a universal and adaptive star identification algorithm is essential to ensure stable and precise attitude determination within the SDS architecture. To this end, this paper proposes a parameter-independent star identification algorithm based on topological invariance features. The method integrates a topological invariant feature coordinate system with a self-organizing map (SOM) neural network, enabling consistent recognition across diverse payload configurations and observation scenarios. Extensive experiments under conditions such as stellar position noise, false-star noise, missing-star noise, focal length variation, and dynamic fields of view demonstrate that the proposed algorithm consistently outperforms three benchmark methods and maintains stable accuracy. These results confirm that the proposed method not only aligns with the SDS philosophy of hardware-software decoupling but also provides a necessary algorithmic foundation for its flexible, reconfigurable, and multi-payload attitude determination framework.

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%.

This paper presents a novel approach to the attitude determination problem of space vehicles. The proposed algorithm utilizes a modified Genetic Algorithm (GA) to solve the “lost in space” star pattern recognition problem associated with star tracker attitude determination systems. Characteristics of the stars that are visible within the Field of View (FOV) – reflected on an image taken by the onboard star tracker – are formulated using simple geometric descriptions. The proposed GA minimizes the discrepancy between the characteristics of the stars inside the actual FOV and a candidate FOV selected from the on board stored star map. The global minimum of the discrepancy represents the inertial coordinates of the FOV bore-sight. The concept of a Spiral Genetic Algorithms (SGA) is proposed where the search area decreases for consecutive GA, with consequently tighter constraints, making it converge to the desired location. Also the algorithm presented has the capability of determining the rotational angle between the spacecraft’s coordinate system and that of a real star map. Simulation results indicate competitive results to current star trackers in terms of accuracy.

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.