Improving night sky star image processing algorithm for star sensors

Arbabmir, Mohammad Vali; Mohammadi, Seyyed Mohammad; Salahshour, Sadegh; Somayehee, Farshad

doi:10.1364/josaa.31.000794

Cited by 26 publications

(17 citation statements)

References 5 publications

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“…Lastly, estimate the length and the width of the point cloud as the length and the width of the extended target. Given all the plots occupied by a target, methods in [16][17][18] can be used to calculate the accurate location of the target. Meanwhile, (7)-(9) are also widely used in centroid extraction.…”

Section: Traditional Methodsmentioning

confidence: 99%

“…And the methods proposed in [14,15] are used to distinguish ships from strong clutter. As to the last step, centroid extraction, an improved centroid extraction algorithm for autonomous star sensor is proposed in [16]; its computational complexity is lower than the algorithm proposed in [17].…”

Section: Introductionmentioning

confidence: 99%

“…Most calculations are spent on the second step, obtaining the clusters where each cluster is the set of plots occupied by one extended target. An improved centroid extraction algorithm for autonomous star sensor is proposed in [16], which has a lower computational complexity compared with the centroid extraction algorithm proposed in [17]. And the entire stellar image is necessary in both methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Plot Fusion Method for High Resolution Radar Based on Contour Tracking Algorithm

Yan

Zhao

et al. 2016

International Journal of Antennas and Propagation

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With the development of radar system, the problem of enormous raw data has drawn much attention. A plot fusion method based on contour tracking algorithm is proposed to detect extended targets in a radar image. Firstly, the characteristic of radar image in complex environment is revealed. Then, the steps of traditional method, region growing method, and the proposed method are introduced. Meanwhile, the algorithm of tracking the contour of an extended target is illustrated in detail. It is not necessary to scan all the plots in the image, because the size of target is considered in the proposed method. Therefore, the proposed method is much more efficient than several existing methods. Lastly, the performance of several methods is tested using the raw data of two scenarios in real world. The experiment results show that the proposed method is practical and most likely to satisfy the real-time requirement in various complex environment.

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Section: Traditional Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Plot Fusion Method for High Resolution Radar Based on Contour Tracking Algorithm

Yan

Zhao

et al. 2016

International Journal of Antennas and Propagation

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“…The thresholding extracts the signal pixels from noise background and there are two types of thresholding global and local [16]. A star spreads over a number of pixels due to slight defocusing and all these pixels are grouped into a single star using functions like labeling and clustering [17]. The pixel size and focal length limit the star tracker accuracy.…”

Section: Space Cameramentioning

confidence: 99%

Star Selection algorithm for Arcsecond Pico Star Tracker

Muruganandan

Park

Lee

et al. 2018

2018 AIAA Aerospace Sciences Meeting

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The star tracker estimates pointing knowledge of a satellite in arcsecond accuracy in three axes without apriori knowledge. But star trackers are larger in size, heavier, power hungry and expensive for nanosatellite missions. The Arcsecond Pico Star Tracker (APST) is designed based on the limitations of nanosatellites and estimated to provide pointing knowledge in arcsecond. The APST is developed using fully COTS components because it's affordable, and less development time. A theoretical model is developed to estimate the performance of the COTS components (image sensor, lens, and baffle) used in the APST. Using this model, it's possible to validate if the components meet the requirements of the star tracker. But COTS component decreases the overall performance due to the errors in image sensor noise, lens distortion, and aberration etc. In APST, the lens distortion and inaccurate centroiding are the dominant error sources. The radial lens distortion causes an error in angular distance measurement, which leads misidentifying or identification of stars and high processing time. This leads to functional failure of APST. To overcome this, the relative star selection method is iv developed which selects the stars based on the angular distance information. Based on the fact that star pair with low angular distance has minimum measurement error, the relative star selection selects the four stars with low measurement error. It's compared with conventional bright star selection method, whereas stars are selected based on brightness. The relative selection algorithm is tested with 75-star constellation in star simulator and it has delivered 100% success rate and accuracy of 71 arcseconds in boresight. Whereas the conventional bright star selection delivered low success rate of 28% because the star pairs are not selected based on angular distance separation. Hence the relative star selection algorithm is efficient for APST.

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“…The most important function for a star sensor to determine the attitude is star identification. Star identification in the LIS mode is much more difficult than in the tracking mode [5][6][7][8]. As a result, there are many different algorithms for star identification within an FOV (see review paper [9]).…”

Section: Introductionmentioning

confidence: 99%

Lost-in-Space Star Identification Using Planar Triangle Principal Component Analysis Algorithm

Zhou

2015

Mathematical Problems in Engineering

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It is a challenging task for a star sensor to implement star identification and determine the attitude of a spacecraft in the lost-in-space mode. Several algorithms based on triangle method are proposed for star identification in this mode. However, these methods hold great time consumption and large guide star catalog memory size. The star identification performance of these methods requires improvements. To address these problems, a star identification algorithm using planar triangle principal component analysis is presented here. A star pattern is generated based on the planar triangle created by stars within the field of view of a star sensor and the projection of the triangle. Since a projection can determine an index for a unique triangle in the catalog, the adoption of thek-vector range search technique makes this algorithm very fast. In addition, a sharing star validation method is constructed to verify the identification results. Simulation results show that the proposed algorithm is more robust than the planar triangle andP-vector algorithms under the same conditions.

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Improving night sky star image processing algorithm for star sensors

Cited by 26 publications

References 5 publications

An Efficient Plot Fusion Method for High Resolution Radar Based on Contour Tracking Algorithm

An Efficient Plot Fusion Method for High Resolution Radar Based on Contour Tracking Algorithm

Star Selection algorithm for Arcsecond Pico Star Tracker

Lost-in-Space Star Identification Using Planar Triangle Principal Component Analysis Algorithm

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