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

Gong, Jiaqi; Wu, Lin; Gong, Junbin; Ma, Jie; Tian, Jinwen

doi:10.1117/1.3269686

Cited by 15 publications

(5 citation statements)

References 16 publications

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“…Two sets of simulations were devised and executed to validate the performance of the recognition approach. Because of the influence of the space environment and the star sensor system itself [28,30], background noise in a star sensor is inevitable. It is difficult to construct a precise model that accounts for background noise because a variety of sources contribute to this noise.…”

Section: Simulation Of Target Recognition and Discussionmentioning

confidence: 99%

Autonomous space target recognition and tracking approach using star sensors based on a Kalman filter

Tao

Zhou

2015

Appl. Opt.

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When imaged by detectors, space targets (including satellites and debris) and background stars have similar point-spread functions, and both objects appear to change as detectors track targets. Therefore, traditional tracking methods cannot separate targets from stars and cannot directly recognize targets in 2D images. Consequently, we propose an autonomous space target recognition and tracking approach using a star sensor technique and a Kalman filter (KF). A two-step method for subpixel-scale detection of star objects (including stars and targets) is developed, and the combination of the star sensor technique and a KF is used to track targets. The experimental results show that the proposed method is adequate for autonomously recognizing and tracking space targets.

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Section: Simulation Of Target Recognition and Discussionmentioning

confidence: 99%

Autonomous space target recognition and tracking approach using star sensors based on a Kalman filter

Tao

Zhou

2015

Appl. Opt.

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“…This identification strategy requires additional checking to preserve integrity. A log-polar transforming-based star pattern was introduced in [15], while a star pattern called flower code was presented in [16]. These patterns are less efficient than others because of their cyclic dynamic match step.…”

Section: Index Termsmentioning

confidence: 99%

Improved Grid Algorithm Based on Star Pair Pattern and Two-dimensional Angular Distances for Full-Sky Star Identification

Wei

Wang

et al. 2020

IEEE Access

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“…In addition, the star sensor's sensitivity calibration error may bring in star magnitude noise. Most algorithms [6,[22][23][24][25][26] test their robustness by adding pixel and magnitude noise on the star image. In this paper, we still adopt this way to test the robustness of our algorithm.…”

Section: Simulation and Analysismentioning

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

Cited by 15 publications

References 16 publications

Autonomous space target recognition and tracking approach using star sensors based on a Kalman filter

Autonomous space target recognition and tracking approach using star sensors based on a Kalman filter

Improved Grid Algorithm Based on Star Pair Pattern and Two-dimensional Angular Distances for Full-Sky Star Identification

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

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