Real Star Images Research Articles

Weak Spatial Target Extraction Based on Small-Field Optical System.

Compared to wide-field telescopes, small-field detection systems have higher spatial resolution, resulting in stronger detection capabilities and higher positioning accuracy. When detecting by small fields in synchronous orbit, both space debris and fixed stars are imaged as point targets, making it difficult to distinguish them. In addition, with the improvement in detection capabilities, the number of stars in the background rapidly increases, which puts higher requirements on recognition algorithms. Therefore, star detection is indispensable for identifying and locating space debris in complex backgrounds. To address these difficulties, this paper proposes a real-time star extraction method based on adaptive filtering and multi-frame projection. We use bad point repair and background suppression algorithms to preprocess star images. Afterwards, we analyze and enhance the target signal-to-noise ratio (SNR). Then, we use multi-frame projection to fuse information. Subsequently, adaptive filtering, adaptive morphology, and adaptive median filtering algorithms are proposed to detect trajectories. Finally, the projection is released to locate the target. Our recognition algorithm has been verified by real star images, and the images were captured using small-field telescopes. The experimental results demonstrate the effectiveness of the algorithm proposed in this paper. We successfully extracted hip-27066 star, which has a magnitude of about 12 and an SNR of about 1.5. Compared with existing methods, our algorithm has advantages in both recognition rate and false-alarm rate, and can be used as a real-time target recognition algorithm for space-based synchronous orbit detection payloads.

An Autonomous Global Star Identification Algorithm Based on the Fast MST Index and Robust Multi-Order CCA Pattern

Star identification plays a key role in spacecraft attitude measurement. Currently, most star identification algorithms tend to perform well only in a scene without noise and are highly sensitive to noise. To solve this problem, this paper proposes a star identification algorithm based on the maximum spanning tree (MST) index and multi-order continuous cycle angle (CCA) intended for the lost-in-space mode. In addition, a neighboring star selection method named dynamic eight-quadrant (DEQ) is developed. First, the DEQ method is used to select high-confidence neighboring stars for the main star. Then, the star image is regarded as a graph, and the Prim algorithm is employed to construct the MST pattern for each guide star, which is then combined with the K vector index to perform the main star candidate search. Finally, the Jackard similarity voting for the multi-order CCA of the main star is used to identify the main star, and the global neighboring star identification is conducted by the multi-order CCA of neighboring stars. The simulated and real star images test results show that compared with five mainstream algorithms, when the position noise is 1 pixel, the number of false stars is five, the magnitude noise is 0.5, and the identification accuracy of the proposed algorithm is higher than 98.5%. Therefore, the proposed algorithm has excellent anti-noise ability in comparison to other algorithms.

RPNet: A Representation Learning-Based Star Identification Algorithm

A novel star identification network (RPNet) based on representation learning is proposed in this paper. Unlike other pattern-based stars identification algorithms, the RPNet does not require the creation of an elaborate pattern, nor does it need to search among patterns. Instead, a star pattern generator (SPG) in the RPNet helps in finding the best pattern that can distinguish different stars clearly. A star pattern classifier (SPC) in the RPNet is utilized to recognize the pattern generated before. The simulations show that the RPNet is extremely robust toward star position noise, star magnitude noise, and false stars. The performance on simulation images outperforms almost all other pattern-based stars identification algorithms. On average, it achieves an identification rate of 99.23% in simulated star images. The identification rate on real star images is higher than 98%. Moreover, the algorithm achieves this performance with lesser memory and faster speed compared to polygon algorithms.

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.

The image stitching algorithm based on aggregated star groups

The star map has the characteristics of large amount of data and wide angle of view. The star points are displayed as limited pixels on the image and easy to get disturbed by noise, so it is difficult to build a feature model to obtain the complete star map. In this paper, we have revised the OTSU algorithm and extracted the star points accurately according to the characteristics of star point pixels. We are inspired by the idea of compressed perception to break the local spatial relationship of stars, construct the aggregated star group model of relative relationship of local stars, realize the matching and construct the extended model to realize the stitching of wide angle star images. In order to meet the requirements of efficient transmission and storage in the air or on the ground, we have proposed the algorithm of star point storage and mapping. We set up a database with 600 frames of real star images from CCD camera and 600 frames of simulated star images, and the experiments show that our algorithm can compress the data and realize the star image stitching.

A robust star identification algorithm with star shortlisting

A star tracker provides the most accurate attitude solution in terms of arc seconds compared to the other existing attitude sensors. When no prior attitude information is available, it operates in “Lost-In-Space (LIS)” mode. Star pattern recognition, also known as star identification algorithm, forms the most crucial part of a star tracker in the LIS mode. Recognition reliability and speed are the two most important parameters of a star pattern recognition technique. In this paper, a novel star identification algorithm with star ID shortlisting is proposed. Firstly, the star IDs are shortlisted based on worst-case patch mismatch, and later stars are identified in the image by an initial match confirmed with a running sequential angular match technique. The proposed idea is tested on 16,200 simulated star images having magnitude uncertainty, noise stars, positional deviation, and varying size of the field of view. The proposed idea is also benchmarked with the state-of-the-art star pattern recognition techniques. Finally, the real-time performance of the proposed technique is tested on the 3104 real star images captured by a star tracker SST-20S currently mounted on a satellite. The proposed technique can achieve an identification accuracy of 98% and takes only 8.2 ms for identification on real images. Simulation and real-time results depict that the proposed technique is highly robust and achieves a high speed of identification suitable for actual space applications.

A voting-based star identification algorithm utilizing local and global distribution

A novel star identification algorithm based on voting scheme is presented in this paper. In the proposed algorithm, the global distribution and local distribution of sensor stars are fully utilized, and the stratified voting scheme is adopted to obtain the candidates for sensor stars. The database optimization is employed to reduce its memory requirement and improve the robustness of the proposed algorithm. The simulation shows that the proposed algorithm exhibits 99.81% identification rate with 2-pixel standard deviations of positional noises and 0.322-Mv magnitude noises. Compared with two similar algorithms, the proposed algorithm is more robust towards noise, and the average identification time and required memory is less. Furthermore, the real sky test shows that the proposed algorithm performs well on the real star images.

Star tracking method based on multiexposure imaging for intensified star trackers.

The requirements for the dynamic performance of star trackers are rapidly increasing with the development of space exploration technologies. However, insufficient knowledge of the angular acceleration has largely decreased the performance of the existing star tracking methods, and star trackers may even fail to track under highly dynamic conditions. This study proposes a star tracking method based on multiexposure imaging for intensified star trackers. The accurate estimation model of the complete motion parameters, including the angular velocity and angular acceleration, is established according to the working characteristic of multiexposure imaging. The estimation of the complete motion parameters is utilized to generate the predictive star image accurately. Therefore, the correct matching and tracking between stars in the real and predictive star images can be reliably accomplished under highly dynamic conditions. Simulations with specific dynamic conditions are conducted to verify the feasibility and effectiveness of the proposed method. Experiments with real starry night sky observation are also conducted for further verification. Simulations and experiments demonstrate that the proposed method is effective and shows excellent performance under highly dynamic conditions.

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.

Compressive measurement and feature reconstruction method for autonomous star trackers

Compressive sensing (CS) theory provides a framework for signal reconstruction using a sub-Nyquist sampling rate. CS theory enables the reconstruction of a signal that is sparse or compressible from a small set of measurements. The current CS application in optical field mainly focuses on reconstructing the original image using optimization algorithms and conducts data processing in full-dimensional image, which cannot reduce the data processing rate. This study is based on the spatial sparsity of star image and proposes a new compressive measurement and reconstruction method that extracts the star feature from compressive data and directly reconstructs it to the original image for attitude determination. A pixel-based folding model that preserves the star feature and enables feature reconstruction is presented to encode the original pixel location into the superposed space. A feature reconstruction method is then proposed to extract the star centroid by compensating distortions and to decode the centroid without reconstructing the whole image, which reduces the sampling rate and data processing rate at the same time. The statistical results investigate the proportion of star distortion and false matching results, which verifies the correctness of the proposed method. The results also verify the robustness of the proposed method to a great extent and demonstrate that its performance can be improved by sufficient measurement in noise cases. Moreover, the result on real star images significantly ensures the correct star centroid estimation for attitude determination and confirms the feasibility of applying the proposed method in a star tracker.

A Discrete HMM-Based Feature Sequence Model Approach for Star Identification

This paper presents a novel star pattern recognition algorithm based on a discrete hidden Markov model (HMM) for autonomous spacecraft attitude determination in the lost-in-space mode. A two-layer structure is proposed to build an HMM-based star pattern for every guide star. The hidden layer describes the unique geometric distributions of stars in the field of view via the transitions among hidden states, and the observation layer uses discrete and rounded feature sequences consisting of the angular distances of star pairs as outputs. An HMM-based star pattern with maximum probabilities in generating the sequences obtained in the recognition process is uniquely matched. Experiments of synthesized and real star images are tested for the HMM algorithm. Results show that the proposed algorithm has a fast average identification time and is highly robust toward star positions, magnitude noise, and false stars. In the simulation, the HMM algorithm exhibits 99.65% identification rate with 0.4 pixel standard deviations of positional noises, 0.323-Mv magnitude noises, and 1.5-ms average identification time. Moreover, the identification rate is 99.11% and the average identification time is 1.2 ms when testing real star images.

Redundant-coded radial and neighbor star pattern identification algorithm

Aimed at solving the “lost-in-space” (LIS) problem, a robust star pattern identification algorithm is presented. The neighbor star pattern is introduced to increase separating ability and stabilization of star patterns and reduce the effects of magnitude noise. Redundant coding for radial and neighbor star patterns is proposed to more effectively remove the effects of position noise. Binary bit strings are used for storing the redundant code to reduce the size of the on-board database. A similar score measurement is proposed on the basis of shift and logical AND operations to quickly implement the star's initial match. The final match results are obtained using a strategy of searching for the longest matching chain. The algorithm is evaluated with synthesized and real star images. The identification rates of this algorithm are 97.98% with standard deviations of position noises of 1 pixel and 94.96% with standard deviations of magnitude noises of 0.646 Mv. Moreover, the identification rate is 77.12% when there are only four stars in the field of view (FOV). The algorithm obtains an average identification rate of 96.96% from real star images.

Motion-blurred star acquisition method of the star tracker under high dynamic conditions

The star tracker is one of the most promising attitude measurement devices used in spacecraft due to its extremely high accuracy. However, high dynamic performance is still one of its constraints. Smearing appears, making it more difficult to distinguish the energy dispersive star point from the noise. An effective star acquisition approach for motion-blurred star image is proposed in this work. The correlation filter and mathematical morphology algorithm is combined to enhance the signal energy and evaluate slowly varying background noise. The star point can be separated from most types of noise in this manner, making extraction and recognition easier. Partial image differentiation is then utilized to obtain the motion parameters from only one image of the star tracker based on the above process. Considering the motion model, the reference window is adopted to perform centroid determination. Star acquisition results of real on-orbit star images and laboratory validation experiments demonstrate that the method described in this work is effective and the dynamic performance of the star tracker could be improved along with more identified stars and guaranteed position accuracy of the star point.

Flower algorithm for star pattern recognition in space surveillance with star trackers

Using star tracker to perform space surveillance is a focal point of research in aerospace engineering. However, autonomous attitude determination with star trackers in missions is a challenging task, because of spacecraft attitude dynamics and false stars. We present a novel star pattern recognition algorithm to resolve these problems. The algorithm defines a star pattern, called a flower code, composed of angular distances and circular angles. Then, a three-step strategy is adopted to find the correspondence of the sensor pattern and the catalog pattern, including initial lookup table match, cyclic dynamic match, and validation. A number of experiments are carried out on simulated and real star images. The simulation results show that the proposed method provides improved performance, especially on robustness against false stars. Also, the results for real star images demonstrate the reliability of the method for ground-based measurements.

Algorithm for CCD Star Image Rapid Locating

摘要: 大尺寸CCD星图快速处理是制约天基光学观测器件性能的关键因素之一,在星敏感器、空间监视载荷等研制中具有重要意义.通过对CCD星图图像特点的分析,提出了以最大概率实现背景噪声滤除的星图预处理准则;在此基础上,提出了通过两次投影检测实现星点位置测量的算法.与传统方法相比,该算法实现简单,节约了计算量和存储量.对分辨率为2048×2048的CCD实拍星图的实验研究表明了该算法的有效性. 关键词: 图像处理 / 星图 / 星敏感器 / 空间监视

Extreme M Dwarfs and Brown Dwarfs in a Deep CCD Survey

Two years ago, Seitzer and Tyson began a program of 4-meter prime focus CCD observations at CTIO, with the aim to develop techniques for imaging and photometry to the theoretical limit of that telescope and overall 52% efficiency: 26.6 J mag, 26 R mag, 25 I mag. Color-magnitude plots show that the limiting magnitudes for detection are 28 J mag, 27 R mag, and 25.3 I mag. Each of our CCD fields covers about 12 sq. arcmin., with total exposure time about 7000 sec. in each of the three bands (see Boeshaar and Tyson 1985). Processed images are put on a VAX 11/780 and the FOCAS v.3.2 automated detector and classifier is run. In order to determine the probability of detection and photometric errors as a function of magnitude, FOCAS is rerun many times on artificial images made by adding in real star and galaxy images, dimmed many magnitudes, at random locations on the CCD sky frame. Over 2000 objects are detected in every high galactic latitude CCD field, less than 50 of which are classified as stars. Most of the faint galaxies are very blue (due to evolution), making it possible to search for infrared excess stars in the presence of so many galaxies.