Star Centroid Research Articles

Restoration Method of a Blurred Star Image for a Star Sensor Under Dynamic Conditions.

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.

Near infrared star centroid detection by area analysis of multi-scale super pixel saliency fusion map

The centroid location of a near infrared star always deviates from the real center due to the effects of surrounding radiation. To determine a more accurate center of a near infrared star, this paper proposes a method to detect the star's saliency area and calculate the star's centroid via the pixels only in this area, which can greatly decrease the effect of the radiation. During saliency area detection, we calculated the boundary connectivity and gray similarity of every pixel to estimate how likely it was to be a background pixel. Aiming to simplify and speed up the calculation process, we divided the near infrared starry sky image into super pixel maps at multi-scale by Simple Linear Iterative Clustering (SLIC). Second, we detected the saliency map for every super pixel map of the image. Finally, we fused the saliency maps according to a weighted coefficient that is determined by the least square method. For the images used in our experiment, we set the multi-scale super pixel numbers to 100, 150, and 200. The results show that our method can obtain an offset variance of less than 0.27 for the center coordinates compared to the labelled centers.

Multi-Stage Star Image Identification Method of Three Field-of-View Star Sensor

To improve the low efficiency and low navigation star identification rate of existing star image identification methods for three field-of-view (FOV) star sensor, a multi-stage star image identification method is proposed. Firstly, the generalized regression neural network which has only one adjustable parameter, is used to identify the star images in each field-of-view. Secondly, the star angular distance saved in the navigation star database is used to verify the identification results, and then the optical directions of the three FOVs are calculated by using the correctly identified navigation stars. Thirdly, the optical directions are utilized to auxiliary correct the unidentified and erroneous identified navigation stars. Finally, the high-accuracy probe attitude is estimated by using the correctly identified navigation stars in the three FOVs. The simulation results show that the identification rates of the experimental samples is of 98.9% when the standard deviation of star centroid positioning error increases to 0.07 pixels, but the identification time is only of 8.464 5 ms. Meanwhile, since the three field-of-view star sensor captures the more dispersed navigation stars, the probe attitude accuracy of yaw, pitch and roll angles by using the present method is improved evidently, which is of 1.205 8″, 1.086 7″, and 1.201 8″, respectively.

Star Image Prediction and Restoration under Dynamic Conditions.

The star sensor is widely used in attitude control systems of spacecraft for attitude measurement. However, under high dynamic conditions, frame loss and smearing of the star image may appear and result in decreased accuracy or even failure of the star centroid extraction and attitude determination. To improve the performance of the star sensor under dynamic conditions, a gyroscope-assisted star image prediction method and an improved Richardson-Lucy (RL) algorithm based on the ensemble back-propagation neural network (EBPNN) are proposed. First, for the frame loss problem of the star sensor, considering the distortion of the star sensor lens, a prediction model of the star spot position is obtained by the angular rates of the gyroscope. Second, to restore the smearing star image, the point spread function (PSF) is calculated by the angular velocity of the gyroscope. Then, we use the EBPNN 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 and real-time of the proposed algorithm.

Star Detection and Accurate Centroiding for the Geosynchronous Interferometric Infrared Sounder of Fengyun-4A

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.

Accurate and Robust Synchronous Extraction Algorithm for Star Centroid and Nearby Celestial Body Edge

Celestial body features are important navigation information in deep space exploration. This study provides a synchronous high-precision extraction algorithm for star centroid and nearby celestial body edges for a miniaturized independent optical navigation sensor, which combines the functions of a star tracker and a navigation camera. The image is filtered by a ring filter template to eliminate the interference information of background and improve the contrast between the target and the background. The second-order directional derivative and specific area characteristic method aim to roughly extract and distinguish the features (star centroid and the nearby celestial body edge). In local area template where feature points are located, the 1D energy deviation effect is proposed to extract the features of the two different light intensity distribution models. The accuracy and robustness of our algorithm are verified by simulation and ground-based experiments. The algorithm has certain reference significance for other types of dim target and edge detections, such as infrared detection, medical image, target measurement, and machine vision.

精细导星仪星点定位系统误差的高精度补偿方法

精细导星仪星点定位系统误差的高精度补偿方法

Blind Deblurring Using Space Target Features

The star image suffers inevitably from degradation due to the high-speed motion of the space target and the long exposure time of the camera, therefore the attitude information of the star is hard to accurately obtain. This paper proposes a blind deblurring algorithm that combines shape features of space targets. First, the astronomical image is preprocessed using saliency detection. Then, considering the shape characteristics of space targets, the Minimum Bounding Rectangle (MBR) is introduced to describe the space targets. Next, the parameters of the MBR are used to estimate the point spread function (PSF). Finally, a regularization method is employed to recover the astronomical images. Experimental results on both simulation and real star images demonstrate that the proposed method reduces the error of point spread function estimation and decreases the error rate of identified space targets. At the same time, the accuracy of the star centroid extraction improves 0.2786 on average and the error of the star centroid location extraction reduces 0.059 comparing to the state-of-art method. The proposed method is of great significance for positioning, recognition and attitude determination of space targets.

Distributed Parallel Super-Block-Based Star Detection and Centroid Calculation

Due to the inadequacies of the speed of star detection and centroid calculation (SDCC), the applications of high-resolution and high-frame-rate image sensors are greatly limited in star trackers, restricting the performance of the star trackers. To resolve this problem, a novel distributed parallel super-block-based SDCC method is proposed in this paper. In contrast to the previous SDCC methods, the proposed method divides the star images into several sub-images and processes these sub-images in parallel. Then, by using a super-block-based method and a distributed shared memory-based method to process the stars in sub-images (sub-stars) and in adjacent sub-images (boundary stars), respectively, the position information of all the stars are detected during the parallel scan of the sub-images. The proposed method exhibits a speed that is up to $4 \,\, \times $ M times faster than the previous SDCC methods in processing the star images that are divided into M sub-images. Experimental results demonstrate that the proposed method is correct and effective. It is applicable to the use of most high-performance image sensors and resolves the performance limitation of star trackers to achieve better attitude accuracy and attitude update rate.

A Comprehensive Calibration Method for a Star Tracker and Gyroscope Units Integrated System.

The integration of a star tracker and gyroscope units (GUs) can take full advantage of the benefits of each, and provide continuous and accurate attitude information with a high update rate. The systematic error calibration of the integrated system is a crucial step to guarantee its attitude accuracy. In this paper, a comprehensive calibration method for the star tracker and GUs integrated system is proposed from a global perspective. Firstly, the observation model of the predicted star centroid error (PSCE) with respect to the systematic errors including the star tracker intrinsic parameter errors, GUs errors and fixed angle errors is accurately established. Then, the systematic errors are modeled by a series of differential equations, based on which the state-space model is established. Finally, the systematic errors are decoupled and estimated by a Kalman filter according to the established state-space model and observation model. The coupling between the errors of the principal point and subcomponents of the fixed angles (i.e., and ) is analysed. Both simulations and experiments indicate that the proposed method is effective at estimating the systematic errors of the star tracker and GUs integrated system with high accuracy and robustness with respect to different star centroid accuracies and gyroscope noise levels.

Star Centroiding Based on Fast Gaussian Fitting for Star Sensors.

The most accurate star centroiding method for star sensors is the Gaussian fitting (GF) algorithm, because the intensity distribution of a star spot conforms to the Gaussian function, but the computational complexity of GF is too high for real-time applications. In this paper, we develop the fast Gaussian fitting method (FGF), which approximates the solution of the GF in a closed-form, thus significantly speeding up the GF algorithm. Based on the fast Gaussian fitting method, a novel star centroiding algorithm is proposed, which sequentially performs the FGF twice to calculate the star centroid: the first FGF step roughly calculates the Gaussian parameters of a star spot and the noise intensity of each pixel; subsequently the second FGF accurately calculates the star centroid utilizing the noise intensity provided in the first step. In this way, the proposed algorithm achieves both high accuracy and high efficiency. Both simulated star images and star sensor images are used to verify the performance of the algorithm. Experimental results show that the accuracy of the proposed algorithm is almost the same as the GF algorithm, higher than most existing centroiding algorithms, meanwhile, the proposed algorithm is about 15 times faster than the GF algorithm, making it suitable for real-time applications.

Motion Blurred Star Image Restoration Based on MEMS Gyroscope Aid and Blur Kernel Correction.

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.

Kinematics of Stars from the TGAS (Gaia DR1) Catalogue

Based on the stellar proper motions of the TGAS (Gaia DR1) catalogue, we have analyzed the velocity field of main-sequence stars and red giants from the TGAS catalogue with heliocentric distances up to 1.5 kpc. We have obtained four variants of kinematic parameters corresponding to different methods of calculating the distances from the parallaxes of stars measured with large relative errors. We have established that within the Ogorodnikov–Milne model changing the variant of distances affects significantly only the solar velocity components relative to the chosen centroid of stars, provided that the solution is obtained in narrow ranges of distances (0.1 kpc). The estimates of all the remaining kinematic parameters change little. This allows the Oort coefficients and related Galactic rotation parameters as well as all the remaining Ogorodnikov–Milne model parameters (except for the solar terms) to be reliably estimated irrespective of the parallax measurement accuracy. The main results obtained from main-sequence stars in the range of distances from 0.1 to 1.5 kpc are: A = 16.29 ± 0.06 km s−1 kpc−1, B = −11.90 ± 0.05 km s−1 kpc−1, C = −2.99 ± 0.06 km s−1 kpc−1, K = −4.04 ± 0.16 km s−1 kpc−1, and the Galactic rotation period P = 217.41 ± 0.60 Myr. The analogous results obtained from red giants in the range from 0.2 to 1.6 kpc are: the Oort constants A = 13.32 ± 0.09 km s−1 kpc−1, B = −12.71 ± 0.06 km s−1 kpc−1, C = −2.04 ± 0.08 km s−1 kpc−1, K = −2.72 ± 0.19 km s−1 kpc−1, and the Galactic rotation period P = 236.03 ± 0.98 Myr. The Galactic rotation velocity gradient along the radius vector (the slope of the Galactic rotation curve) is −4.32 ± 0.08 km s−1 kpc−1 for main-sequence stars and −0.61 ± 0.11 km s−1 kpc−1 for red giants. This suggests that the Galactic rotation velocity determined from main-sequence stars decreases with increasing distance from the Galactic center faster than it does for red giants.

High-Accuracy Synchronous Extraction Algorithm of Star and Celestial Body Features for Optical Navigation Sensor

This paper provides an optimal high-performance image processing algorithm for a miniaturized independent optical navigation sensor, which combines the functions of a star tracker and a navigation camera. This novel image processing algorithm is capable of extracting two different types of optical navigation measurements from a raw image. The aim is to simultaneously extract stars and target celestial body features with high accuracy and reliability to estimate observer-to-body relative position in subsequent navigation process. This paper presents star and celestial body imaging models and a novel slope edge model. We propose an high-performance algorithm to achieve the synchronous extraction of star and celestial body image features based on the aforementioned models. Double-window variance difference method is proposed to segment and classify stars and edge image regions of a celestial body with strong robustness. The sub-pixel level position of star centroid and celestial body edges are then simultaneously extracted by using the same operator on the basis of the consistency of the derivative distribution of star and celestial body edge profiles. The edge extraction deviation when using the slope edge model is also analyzed and compensated, and the accuracy of the celestial body edge extraction is improved to a higher level. The proposed algorithm has excellent feature extraction performance in terms of qualitative and quantitative measurements. This paper has established a technical foundation for the development of the miniaturized independent optical navigation sensor, which is low cost, light weight and has flexible applicability due to its high accuracy and robustness.

Accurate Star Centroid Detection for the Advanced Geosynchronous Radiation Imager of Fengyun-4A

Star observation can be used for the image navigation of certain instruments aboard the three-axis stabilized geostationary satellites. How to extract the accurate star centroids in the observed star images is one of the key problems. In this paper, a high precision star centroid detection method is proposed for the advanced geosynchronous radiation imager (AGRI) of Fengyun-4A (FY-4A), the first experimental satellite of the new generation of Chinese geostationary meteorological satellites FY-4 series. Different from star sensors which are deliberately defocused with relatively large star spot, AGRI is focused for the purpose of earth observation, making it challenging to extract accurate star centroids. To solve the problem, we take the advantage of continuous observing and improve the precision of star centroiding by trajectory fitting and energy response curve fitting. Extensive experiments have been performed on simulated star images and the actual observation data of AGRI aboard FY-4A over ten months in-orbit tests. Experimental results show that the proposed method can accurately extract star centroids with the error less than 0.3 pixels, laying a solid foundation for image navigation of FY-4A.

Centroid error compensation method for a star tracker under complex dynamic conditions

The traditional approach of a star tracker for reducing the dynamic error concentrates on a single frame of star images. Through correlating adjacent star images together with their angular relations sensed by a gyroscope unit (GU), the attitude-correlated frames (ACF) approach expands the view from one single frame to frame sequences. However, the star centroid is shifted from the star true position at the center time of the exposure period under complex dynamic conditions, which is called the complex motion induced error (CMIE) in this paper. The CMIE has a large effect on the performance of the ACF approach. This paper presents a method to compensate the CMIE through reconstructing the star trajectory with the angular velocity of the star tracker sensed by a GU, which achieves effective compensation of the CMIE crossing the boresight direction. Since the observation sensitivity to the CMIE along the boresight direction is low, the attitudes from two different fields of view (FOVs) are combined to improve its compensation accuracy. Then the ACF approach is applied to the obtained results where the CMIE has already been compensated completely. Simulations under shipboard dynamic conditions and experiments under oscillating conditions indicate that the proposed method is effective in improving the performance of the ACF approach and reducing the dynamic error of a star tracker under complex dynamic conditions.

A hybrid method for accurate star tracking using star sensor and gyros.

Star tracking is the primary operating mode of star sensors. To improve tracking accuracy and efficiency, a hybrid method using a star sensor and gyroscopes is proposed in this study. In this method, the dynamic conditions of an aircraft are determined first by the estimated angular acceleration. Under low dynamic conditions, the star sensor is used to measure the star vector and the vector difference method is adopted to estimate the current angular velocity. Under high dynamic conditions, the angular velocity is obtained by the calibrated gyros. The star position is predicted based on the estimated angular velocity and calibrated gyros using the star vector measurements. The results of the semi-physical experiment show that this hybrid method is accurate and feasible. In contrast with the star vector difference and gyro-assisted methods, the star position prediction result of the hybrid method is verified to be more accurate in two different cases under the given random noise of the star centroid.

Celestial Object Imaging Model and Parameter Optimization for an Optical Navigation Sensor Based on the Well Capacity Adjusting Scheme.

The simultaneous extraction of optical navigation measurements from a target celestial body and star images is essential for autonomous optical navigation. Generally, a single optical navigation sensor cannot simultaneously image the target celestial body and stars well-exposed because their irradiance difference is generally large. Multi-sensor integration or complex image processing algorithms are commonly utilized to solve the said problem. This study analyzes and demonstrates the feasibility of simultaneously imaging the target celestial body and stars well-exposed within a single exposure through a single field of view (FOV) optical navigation sensor using the well capacity adjusting (WCA) scheme. First, the irradiance characteristics of the celestial body are analyzed. Then, the celestial body edge model and star spot imaging model are established when the WCA scheme is applied. Furthermore, the effect of exposure parameters on the accuracy of star centroiding and edge extraction is analyzed using the proposed model. Optimal exposure parameters are also derived by conducting Monte Carlo simulation to obtain the best performance of the navigation sensor. Finally, laboratorial and night sky experiments are performed to validate the correctness of the proposed model and optimal exposure parameters.

An Accelerated Motion Blurred Star Restoration Based on Single Image

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 a star image restoration algorithm, including blur kernel estimation as well as an accelerated Richardson–Lucy (RL) reconstruction for motion blur star image under high dynamic conditions. First, an improved Radon transform method is presented by introducing a combination of Z-function and double threshold mask that have considerable anti-noise performance. Based on this improved method, the blur kernel of the degraded star image can be obtained by only utilizing a single blurred star image. Furthermore, as the traditional RL is time-consuming, to overcome this shortcoming, an accelerated algorithm based on the second-order vector extrapolation is proposed, offering speedup of 20 times over the original RL and five times over the existing acceleration algorithms. Finally, experiments on simulated as well as real star images demonstrate that the proposed algorithm is effective in improving the dynamic performance of star sensor even under low signal-to-noise ratio conditions, which is of great importance for the further applications of star sensor.

实现弱星提取及质心定位的帧间窗口移位灰度叠加法

实现弱星提取及质心定位的帧间窗口移位灰度叠加法