Information Theoretic Weighting for Robust Star Centroiding

Flewelling, Brien; Mortari, Daniele

doi:10.1007/bf03321167

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Suppose that the high-and low-pass filters have K h and K g non-zero values, respectively. Then, calculating a j +1 and d j +1 FIGURE 8 The flow chart of the SDDWT algorithm from a j requires K h N and K g N multiplications and (K h − 1)N and (K g − 1)N additions, respectively. In the SDDWT algorithm, it is not necessary to compute all results from the highand low-pass filtering; it is only necessary to decompose the signal to the scale of 2 2 .…”

Section: Separation Of Single Starsmentioning

confidence: 99%

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Fast and robust star detection algorithm based on the dyadic wavelet transform

Chen

Zheng

et al. 2022

IET Image Processing

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Star detection is an important part of the spacecraft attitude determination performed by star trackers. The current advanced star detection algorithms can effectively extract star points in complex backgrounds. However, there are still two problems: first, the parameters of the star detection algorithm are not adaptive, which limits its reliability and robustness; second, the high performance and low computational time of algorithms usually cannot be achieved simultaneously. In this work, a rapid star detection algorithm utilizing dyadic wavelet transform (SDDWT) was developed based on the à Trous algorithm. The authors constructed a suitable dyadic wavelet basis and applied the dyadic wavelet transform to the star image. The maximum wavelet coefficients at a certain scale were binarized according to a threshold value to separate stars from the background. The threshold was adaptively determined according to the white Gaussian noise (WGN) power. Finally, a method for separating individual stars was developed. Experiments indicated that the proposed algorithm was able to ensure a low false alarm rate and high detection rate by adaptively threshold value. Moreover, for a star image of 1024×1024, the computational time of the SDDWT algorithm was stable within 0.3 s, which was smaller than other algorithms.

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Section: Separation Of Single Starsmentioning

confidence: 99%

“…Because of its intuitiveness, simplicity, and speed, thresholding is regarded as a core technique in image segmentation [6]. Global thresholding [7][8][9] is the most commonly used approach for star detection. However, it cannot segment stars in images with nonuniform background intensity.…”

Section: Introductionmentioning

confidence: 99%

Fast and robust star detection algorithm based on the dyadic wavelet transform

Chen

Zheng

et al. 2022

IET Image Processing

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“…Besides the above two classes of star centroiding methods, some other new star centroiding methods were proposed recently. For instance, Flewelling in [ 16 ] presented a star centroiding method with information theoretic weighting. Although the method achieves quite high accuracy, the high computational complexity makes it unsuitable for real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Star Centroiding Based on Fast Gaussian Fitting for Star Sensors

Wan

Wang

Wei

et al. 2018

Sensors

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The most accurate star centroiding method for star sensors is the Gaussian fitting (GF) algorithm, because the intensity distribution of a star spot conforms to the Gaussian function, but the computational complexity of GF is too high for real-time applications. In this paper, we develop the fast Gaussian fitting method (FGF), which approximates the solution of the GF in a closed-form, thus significantly speeding up the GF algorithm. Based on the fast Gaussian fitting method, a novel star centroiding algorithm is proposed, which sequentially performs the FGF twice to calculate the star centroid: the first FGF step roughly calculates the Gaussian parameters of a star spot and the noise intensity of each pixel; subsequently the second FGF accurately calculates the star centroid utilizing the noise intensity provided in the first step. In this way, the proposed algorithm achieves both high accuracy and high efficiency. Both simulated star images and star sensor images are used to verify the performance of the algorithm. Experimental results show that the accuracy of the proposed algorithm is almost the same as the GF algorithm, higher than most existing centroiding algorithms, meanwhile, the proposed algorithm is about 15 times faster than the GF algorithm, making it suitable for real-time applications.

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Single-point position estimation in interplanetary trajectories using star trackers

Mortari

Conway

2016

Celest Mech Dyn Astr

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Information Theoretic Weighting for Robust Star Centroiding

Cited by 5 publications

References 16 publications

Fast and robust star detection algorithm based on the dyadic wavelet transform

Fast and robust star detection algorithm based on the dyadic wavelet transform

Star Centroiding Based on Fast Gaussian Fitting for Star Sensors

Single-point position estimation in interplanetary trajectories using star trackers

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