Plume Noise Suppression Algorithm for Missile-Borne Star Sensor Based on Star Point Shape and Angular Distance between Stars

Fan, Qiang; Cai, Zhixu; Wang, Gangyi

doi:10.3390/s19183838

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…Sci. 2019, 9,4751 4 of 21 where f (x) = I(x, x 0 ) ⊗ p(x) (5) where I(x, x 0 ) denotes the ideal star signal intensity function with ideal centroid position x 0 in the x direction of the image plane.…”

Section: Discrete Approximation Errormentioning

confidence: 99%

“…Star sensors provide the most accurate three-axis attitude information when compared with other attitude measurement devices such as the sun sensor, magnetometer, and gyroscope [1,2]. Therefore, star sensors are widely equipped on orbiting satellites and interplanetary spacecraft [3][4][5]. Attitude estimation by star sensors proceeds by comparing star locations in the image taken by star sensors with those in the predefined on-board catalogue.…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Error Compensation Method Based on an Optimized Extreme Learning Machine for Star Sensor Image Centroid Estimation

et al. 2019

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As an important error in star centroid location estimation, the systematic error greatly restricts the accuracy of the three-axis attitude supplied by a star sensor. In this paper, an analytical study about the behavior of the systematic error in the center of mass (CoM) centroid estimation method under different Gaussian widths of starlight energy distribution is presented by means of frequency field analysis and numerical simulations. Subsequently, an optimized extreme learning machine (ELM) based on the bat algorithm (BA) is adopted to predict the systematic error of the actual star centroid position and then compensate the systematic error from the CoM method. In the BA-ELM model, the input weights matrix and hidden layer biases parameters are encoded as microbat’s locations and optimized by utilizing the strong global search capacity of BA, which significantly improves the performance of ELM in terms of prediction accuracy. The simulation result indicates that our method can reduce the systematic error to less than 3.0 × 10−7 pixels, and its compensation accuracy is two or three orders of magnitude higher than that of other methods for estimating a star centroid location under a 3 × 3 pixel sampling window.

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Section: Discrete Approximation Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Systematic Error Compensation Method Based on an Optimized Extreme Learning Machine for Star Sensor Image Centroid Estimation

et al. 2019

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“…Star sensors supply the more precise attitude reference data than other attitude determination devices, such as the sun sensor, magnetometer, and gyroscope [ 1 , 2 , 3 ]. Therefore, star sensors have become popular in attitude determination for interplanetary spacecraft and orbiting satellites in the last two decades [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Star Identification Based on Multilayer Voting Algorithm for Star Sensors

Wei

Wen

Wang

2021

Sensors

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This paper describes the multilayer voting algorithm, a novel autonomous star identification method for spacecraft attitude determination. The proposed algorithm includes two processes: an initial match process and a verification process. In the initial match process, a triangle voting scheme is used to acquire candidates of the detected stars, in which the triangle unit is adopted as the basic voting unit. During the identification process, feature extraction is implemented, and each triangle unit is described by its singular values. Then the singular values are used to search for candidates of the imaged triangle units, which further improve the efficiency and robustness of the algorithm. After the initial match step, a verification method is applied to eliminate incorrect candidates from the initial results and then outputting the final match results of the imaged stars. Experiments show that our algorithm has more robustness to position noise, magnitude noise, and false stars than the other three algorithms, the identification speed of our algorithm is largely faster than the geometric voting algorithm and optimized grid algorithm. However, it takes more memory, and SVD also seems faster.

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“…Nowadays, with the upsurge of satellite constellations, the fast increase in artificial satellites has brought enormous influence on space exploration and astronomical observation experiments. The requirements for star trackers, especially ones with high accuracy and high dynamic performance, are increasing [ 7 , 8 ]. The star image is the only source of data for star tracker, while it is usually rendered useless by various interferences which can affect star spot extraction.…”

Section: Introductionmentioning

confidence: 99%

A SLIC-DBSCAN Based Algorithm for Extracting Effective Sky Region from a Single Star Image

Shi

Zhang

et al. 2021

Sensors

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The star tracker is widely used for high-accuracy missions due to its high accuracy position high autonomy and low power consumption. On the other hand, the ability of interference suppression of the star tracker has always been a hot issue of concern. A SLIC-DBSCAN-based algorithm for extracting effective information from a single image with strong interference has been developed in this paper to remove interferences. Firstly, the restricted LC (luminance-based contrast) transformation is utilized to enhance the contrast between background noise and the large-area interference. Then, SLIC (the simple linear iterative clustering) algorithm is adopted to segment the saliency map and in this process, optimized parameters are harnessed. Finally, from these segments, features are extracted and superpixels with similar features are combined by using DBSCAN (density-based spatial clustering of applications with noise). The proposed algorithm is proved effective by successfully removing large-area interference and extracting star spots from the sky region of the real star image.

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Plume Noise Suppression Algorithm for Missile-Borne Star Sensor Based on Star Point Shape and Angular Distance between Stars

Cited by 7 publications

References 17 publications

A Systematic Error Compensation Method Based on an Optimized Extreme Learning Machine for Star Sensor Image Centroid Estimation

A Systematic Error Compensation Method Based on an Optimized Extreme Learning Machine for Star Sensor Image Centroid Estimation

Star Identification Based on Multilayer Voting Algorithm for Star Sensors

A SLIC-DBSCAN Based Algorithm for Extracting Effective Sky Region from a Single Star Image

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