Star Centroid Extraction Research Articles

Star Image Registration Modeling and Parameter Calibration for a 3CMOS Star Sensor.

This paper presents an image registration method specifically designed for a star sensor equipped with three complementary metal oxide semiconductor (CMOS) detectors. Its purpose is to register the red-, green-, and blue-channel star images acquired from three CMOS detectors, assuring the precision of star image fusion and centroid extraction in subsequent stages. This study starts with a theoretical analysis aimed at investigating the effect of inconsistent three-channel imaging parameters on the position of feature points. Based on this analysis, this paper establishes a registration model for transforming the red- and blue-channel star images into the green channel's coordinate system. Subsequently, the method estimates model parameters by finding a nonlinear least-squares solution. The experimental results demonstrate the correctness of the theoretical analysis and the proposed registration method. This method can achieve subpixel alignment accuracy in both the x and y directions, thus effectively ensuring the performance of subsequent operation steps in the 3CMOS star sensor.

A High-Accuracy Star Centroid Extraction Method Based on Kalman Filter for Multi-Exposure Imaging Star Sensors.

A multi-exposure imaging approach proposed in earlier studies is used to increase star sensors' attitude update rate by N times. Unfortunately, serious noises are also introduced in the star image due to multiple exposures. Therefore, a star centroid extraction method based on Kalman Filter is proposed in this paper. Firstly, star point prediction windows are generated based on centroids' kinematic model. Secondly, the classic centroid method is used to calculate the coarse centroids of the star points within the prediction windows. Lastly, the coarse centroids are, respectively, processed by each Kalman Filter to filter image noises, and thus fine centroids are obtained. Simulations are conducted to verify the Kalman-Filter-based estimation model. Under noises with zero mean and ±0.4, ±1.0, and ±2.5 pixel maximum deviations, the coordinate errors after filtering are reduced to about 37.5%, 26.3%, and 20.7% of the original ones, respectively. In addition, experiments are conducted to verify the star point prediction windows. Among 100 star images, the average proportion of the number of effective star point objects obtained by the star point prediction windows in the total object number of each star image is calculated as only 0.95%. Both the simulated and experimental results demonstrate the feasibility and effectiveness of the proposed method.

Implementation of a real-time star centroid extraction algorithm with high speed and superior denoising ability.

Star tracker is the most precise attitude measuring device, and its advantages include a high resolution and high update rate. Star centroid extraction, which is a very time-consuming process, has great influence on the attitude update rate. This paper proposes a real-time star centroid extraction algorithm based on a field programmable gate array. First, a 1D top-hat filter is used for star segmentation, which is suitable for both uniform and nonuniform background conditions. Second, multichannel image data is reorganized together into a complete frame through image stitching, which prevents the star spots on the channel boundary from being divided into different parts. Finally, star coordinates are extracted by the center-of-mass algorithm. For an image sensor with a resolution of 2048×2048 pixels, simulation results conducted by a ModelSim simulator show that the star centroid processing time of a single frame is roughly 5.2 ms. Real night experiments demonstrate that the standard deviation of a star centroid error is within 10-2 pixel and the standard deviation of attitude is (2.6 2.2 12.0) arcseconds, which proves that the proposed star centroid extraction algorithm can work continuously and stably.

A Real-Time Star Tailing Removal Method Based on Fast Blur Kernel Estimations

The number of star points and the accuracy of star centroid extraction are the key factors that affect the performance of the star sensor under high dynamic conditions. The motion blur results in the star point trailing, which consequently leads to the decline of star centroid extraction accuracy and in some cases lead to extraction failure. In order to improve the dynamic performance of the star sensor, in this work, we propose a real-time star trailing removal method based on fast blur kernel estimation. First, in order to minimize the influence of noise on parameter estimation, we use principal component analysis (PCA) in the dual-frequency spectrum domain to estimate the angle of blur kernel. In addition, an adjustable weighting method is proposed to estimate the length of blur kernel. So, we are able to quickly estimate the high-precision blur kernel based on a single degraded image. Moreover, an area filtering method based on the hyper-Laplacian prior recovery algorithm is also proposed. This algorithm quickly reconstructs the star points in the tracking windows and effectively removes the star point tailing in real time. The computational efficiency of the proposed algorithm is 5 times superior to the traditional method and 15 times superior to the existing accelerated iterative method. The experimental results show that the proposed algorithm removes the trailing star quickly and effectively, under low SNR. In addition, the proposed method effectively improves the number of extracted star points and the accuracies of star centroids.

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.

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.

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.

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.

Hardware Implementation of Fast and Robust Star Centroid Extraction With Low Resource Cost

Star trackers measure the attitude of a spacecraft by matching the stars captured by the camera and those stored in the onboard database, whose directions are already known. The information (i.e., location and brightness) on the stars in the captured image must be correctly and timely provided for star recognition. This process is called star centroid extraction. The hardware implementation of the star centroid extraction algorithm using parallel and pipelined architecture is a proper solution to ensuring higher accuracy as well as lower time cost. However, some limits restrict the performance of these kinds of algorithms. For example, faint stars, disturbing objects (e.g., the moon, bright planets, and so on), and noise pixels are not valid stars but resume a large amount of resource. Some irregularly shaped star spots may cause the algorithms to obtain inaccurate results. To solve these problems, this paper proposes a star centroid extraction method implemented on field programmable gate arrays (FPGAs) with a dynamic rooted tree architecture. In contrast to the traditional connected domain segmentation method, this method merges the equivalence table in the process of scanning, such that only one scan of the image is needed. Moreover, this method profits from a strict equivalence merging logic and can deal with various irregularly shaped star spots. Experiments are performed both on PC simulations and FPGA platforms, and results show that this method achieves good performance at a very low resource cost.

Fast star centroid extraction algorithm with sub-pixel accuracy based on FPGA

Spacecraft's attitude information plays an important role in celestial navigation. The attitude is mainly determined by matching the star's centroid in the obtained image with its corresponding information in star catalog. Generally, the star image can be regarded as a spot with a diameter <5 pixels. Therefore, it is very difficult to extract the star centroid with sub-pixel accuracy, especially in the hardware system, such as FPGAs. The existing spot centroid extraction methods with high accuracy require plenty of pixels to realize the complex computations. Limited to the star's diameter and hardware requirements, such methods are not suitable for star centroid extraction in hardware system. To solve the problem, a two-step extraction method for star centroid with sub-pixel accuracy is proposed. The maximum pixel-level center can be located through zero crossing of the first derivative in a small region. Taking the pixel-level center as the middle of the window with fixed size, the sub-pixel offsets to the sub-pixel center can be calculated using fixed window weighted centroid method. The sub-pixel center of the star is then obtained by adding the offsets to the pixel-level center. This method can be implemented in hardware to increase processing speed, using Verilog hardware description languages. A simulation is performed on computer and FPGA. Experimental results show the excellent performance in accuracy and processing speed of two-step method. In addition, two-step method has strong ability of resisting noise and good robustness compared to other methods.