An Autonomous Predicted Star Locations Algorithm for Star Sensor

Star Sensor is an avionics instrument used to provide the absolute 3-axis attitude of a spacecraft utilizing star observations. It consists of an electronic camera and associated processing electronics. As outcome of advancing state -of-the-art, new generation star sensor features faster, lower cost, power dissipation and size than the first generation star sensor. This paper describes a star sensor anterior image acquisition and pre-processing hardware system based on CMOS image-sensor and FPGA technology. Practically, star images are produced by a simple simulator on PC, acquired by CMOS image sensor, pre-processed by FPGA, saved in SRAM, read out by EPP protocol and validated by a image process software on PC. The hardware part of system acquires images thought CMOS image-sensor controlled by FPGA, then processes image data by a circuit module of FPGA, and save images to SRAM for test. Basic image data for star recognition and attitude determination of spacecrafts are provided by it. As an important reference for developing star sensor prototype, the system validates the performance advantages of new generation star sensor.

At the same orbital height, the radiation environment in SAA region is worse than that in other regions. The main difference is that the region contains high energy particles. CCD star sensors has been developed for use in the attitude determination and control system of satellites, we have processed star sensors flight data and star images acquired by satellites in orbit in an attempt to characterize the performance of CCD star sensors in the abnormal region of the South Atlantic. This paper also introduces the influence of high energy particles exist in the South Atlantic Anomaly on CCD photoelectric imaging detectors. The abnormal phenomena and generation mechanism in the star images has been analyzed. The improvement measures and suggestions for high precision star sensors to avoid or eliminate the influence of SAA has been given finally, which provides reference for improving the performance of star sensors.

The star sensor is widely used in attitude control systems of spacecraft for attitude measurement. However, under dynamic conditions, the star image acquired by star sensor may appear smearing, which leads to a decline in accuracy or failure of star centroid extraction and attitude determination. In order to improve the performance of the star sensor under high dynamic environment, a Richardson-Lucy (RL) algorithm based on radial basis function neural network (RBFNN) is proposed. First, the Point Spread Function (PSF) is calculated by using the angular velocity information provided by the gyroscope. Then, we use the RBFNN to predict the number of iterations required by the RL algorithm to complete the star image deblurring. Finally, simulation experiments are performed to verify the effectiveness of the proposed algorithm.

Star sensor could work independently ignoring the outside interference, but the disadvantages of star sensor are low output frequency and the limitation in cloudy or rainy day. SINS/CNS integrated navigation system exactly makes up for the deficiencies. However, the practical environment of the surface ship with SINS/CNS integrated navigation system is complicated. It will make the star spots in the star image which is captured by star sensor blurred when the ship is in high dynamic motion condition, especially in the rotation motion. This special environment will results in the inaccurate identification of star image and decrease the gesture determination accuracy of star sensor. At the same time, the reliability of SINS/CNS integrated navigation system is much lower than that in static state. To solve this kind of problem, this paper sums up the previous methods and technologies of image processing, considers the complex and special environment where the SINS/CNS integrated navigation system works actually, and proposes a new improved algorithm which is based on one-dimensional Wiener Filter (1DWF). Through the analysis based on the designed experiment, the attitude and position output accuracy of the star sensor is as high as wiener filter. The result of the experiment demonstrates that the new developed algorithm based on 1DWF could be applied to the SINS/CNS integrated navigation system successfully.

Star sensors play an important role in spacecraft attitude determination. It is common for the roll accuracy of a traditional star sensor with single field of view to be significantly worse than the cross-axis accuracy. Many satellites utilize two/three onboard star cameras for their attitude determination in x, y, z 3-axis. The dual-field-of-view star sensor has been proposed, which can image stars from two orthogonal directions on the same CCD simultaneously. This paper focuses on the critical issue of tagging the star spots from one of the fields of view so that these stars can be recognized. Star light from one direction is modulated by an optical grating in order to tag star images from this viewing field and distinguish them from the star images from another direction. Diffraction splits by phase gratings and amplitude gratings are compared in feasibility and complexity by numerical simulations.

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.

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.

Under the dynamic working conditions of a star sensor, motion blur of the star will appear due to its energy dispersion during imaging, leading to the degradation of the star centroid accuracy and attitude accuracy of the star sensor. To address this, a restoration method of a blurred star image for a star sensor under dynamic conditions is presented in this paper. First, a kinematic model of the star centroid and the degradation function of blurred star image under different conditions are analyzed. Then, an improved curvature filtering method based on energy function is proposed to remove the noise and improve the signal-to-noise ratio of the star image. Finally, the Richardson Lucy algorithm is used and the termination condition of the iterative equation is established by using the star centroid coordinates in three consecutive frames of restored images to ensure the restoration effect of the blurred star image and the accuracy of the star centroid coordinates. Under the dynamic condition of 0~4°/s, the proposed algorithm can effectively improve the signal-to-noise ratio of a blurred star image and maintain an error of the star centroid coordinates that is less than 0.1 pixels, which meets the requirement for high centroid accuracy.

The attitude accuracy of a star sensor decreases rapidly when star images become motion-blurred under dynamic conditions. Existing techniques concentrate on a single frame of star images to solve this problem and improvements are obtained to a certain extent. An attitude-correlated frames (ACF) approach, which concentrates on the features of the attitude transforms of the adjacent star image frames, is proposed to improve upon the existing techniques. The attitude transforms between different star image frames are measured by the strap-down gyro unit precisely. With the ACF method, a much larger star image frame is obtained through the combination of adjacent frames. As a result, the degradation of attitude accuracy caused by motion-blurring are compensated for. The improvement of the attitude accuracy is approximately proportional to the square root of the number of correlated star image frames. Simulations and experimental results indicate that the ACF approach is effective in removing random noises and improving the attitude determination accuracy of the star sensor under highly dynamic conditions.

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.

Extracting accurate star centroids in the observed star images is one of the key problems for image navigation of the geosynchronous interferometric infrared sounder (GIIRS) of Fengyun-4A Satellite (FY-4A), the first scientific experimental satellite of the new generation of Chinese geostationary meteorological satellite Fengyun-4 series. Compared with star sensors which are widely used for star observation, it is challenging to detect the $2\times 2$ star spot from the focused star images of GIIRS and calculate the star centroid in high precision. In this paper, we propose a star detection and centroiding method based on trajectory search and trajectory fitting. Since the launch of FY-4A in December 2016, our centroiding method has been tested in-orbit for over two years. The extensive experiments show that the star centroiding error of our method is less than 0.3 pixels, which makes an important contribution to image navigation of FY-4A.

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.

The accuracy of star centroid extraction and identified star number is both crucial features for star sensor precision. Motion blur and fracture are introduced when star sensor works under high dynamic conditions, which affect the accuracy of star centroid extraction and further reduce the precision of the star sensor. To improve the precision of star sensor, this paper proposes an adaptive window star map restoration method based on energy equalization. The local degradation function is estimated for the star point energy distribution region, and dynamic star map simulation and restoration are performed. The simulation process is divided into three steps. Firstly, establish the motion trajectory of the star point centroid in the detector plane during the exposure time, according to the trailing trajectory size and direction adaptive selection window. Secondly, accumulate the star image point energy in the adaptive window to achieve star point recovery, and finally the recovery effect was evaluated by star extraction results.

We have developed a light-shielding technique for a star sensor that can shorten the baffle length. We achieved a baffle length of 120 mm, two-thirds that of a conventional two-stage baffle. The key idea is that the first lens of the imaging optics is designed as a near-hemispherical (NHS) lens that can work as an angle filter; high-incidence-angle rays are not permitted to be transmitted but low-incidence-angle rays from stars can be. A star sensor system with the new light-shielding technique onboard the SERVIS-1 satellite was launched in October 2003 as an experimental device. Though the in-orbit data verified its fundamental performance in capturing star images, undesirable solar background noise was observed in two corners of the field of view. Ray-trace simulations revealed that slight scattered light on the specular baffle surface entered the NHS lens and reached the corners of the image sensor through a multireflection path inside the lens. We redesigned the baffle and confirmed that stray light was reduced below maximum acceptable levels in a ground test. The star sensor with redesigned baffles is planned to be installed as the main attitude sensor for the SERVIS-2 satellite to be launched in 2008 or later.

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