Precise measurement of the light curves for space debris with wide field of view telescope

Sun, Ruijing; Yu, Shengsheng

doi:10.1007/s10509-019-3527-y

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…Therefore, a reasonable space object detection method is needed to coordinate satellite operations by collecting space environment information to avoid collision risks. Optical detection provides a more energy efficient and cost-effective detection method for space situational awareness missions [1] . However, the use of traditional optical cameras is limited in challenging lighting conditions such as high light, low light and motion blur when capturing high-speed moving targets.…”

Section: Introductionmentioning

confidence: 99%

End-to-end space object detection method based on event camera

Zhou,

Bei

2023

International Conference on Precision Instruments and Optical Engineering (PIOE 2023)

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Section: Introductionmentioning

confidence: 99%

End-to-end space object detection method based on event camera

Zhou,

Bei

2023

International Conference on Precision Instruments and Optical Engineering (PIOE 2023)

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“…Wide field small aperture telescopes (WFSATs) are working horses for time-domain astronomy, because it can cover a large field of view with high cadence in a cost effective way (Yuan et al 2008;Cui et al 2008;Glazier et al 2020;Popowicz 2018;Burd et al 2005;Ratzloff et al 2019;Sun & Yu 2019). As the number of WFSATs increases, the data volume also increases.…”

Section: Introductionmentioning

confidence: 99%

The Deep Neural Network based Photometry and Astrometry Framework for Wide Field Small Aperture Telescopes

Jia¹,

Sun²,

Yang³

et al. 2021

Preprint

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Wide field small aperture telescopes (WFSATs) are mainly used to obtain scientific information of point-like and streak-like celestial objects. However, qualities of images obtained by WFSATs are seriously affected by the background noise and variable point spread functions. Developing high speed and high efficiency data processing method is of great importance for further scientific research. In recent years, deep neural networks have been proposed for detection and classification of celestial objects and have shown better performance than classical methods. In this paper, we further extend abilities of the deep neural network based astronomical target detection framework to make it suitable for photometry and astrometry. We add new branches into the deep neural network to obtain types, magnitudes and positions of different celestial objects at the same time. Tested with simulated data, we find that our neural network has better performance in photometry than classical methods. Because photometry and astrometry are regression algorithms, which would obtain high accuracy measurements instead of rough classification results, the accuracy of photometry and astrometry results would be affected by different observation conditions. To solve this problem, we further propose to use reference stars to train our deep neural network with transfer learning strategy when observation conditions change. The photometry framework proposed in this paper could be used as an end-to-end quick data processing framework for WFSATs, which can further increase response speed and scientific outputs of WFSATs.

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“…These properties make WFSATs light-weighted and low-cost. With remote control, WFSATs are widely used in optical observations for time domain astronomy (Burd et al 2005;Ma et al 2007;Ping & Zhang 2017;Ratzloff et al 2019;Sun & Yu 2019). Meanwhile since WFSATs are normally working automatically and no wavefront sensors are installed in them, they are hard to be maintained timely.…”

Section: Introductionmentioning

confidence: 99%

Point spread function modelling for wide-field small-aperture telescopes with a denoising autoencoder

Jia

et al. 2020

Monthly Notices of the Royal Astronomical Society

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The point spread function reflects the state of an optical telescope and it is important for data post-processing methods design. For wide field small aperture telescopes, the point spread function is hard to model, because it is affected by many different effects and has strong temporal and spatial variations. In this paper, we propose to use the denoising autoencoder, a type of deep neural network, to model the point spread function of wide field small aperture telescopes. The denoising autoencoder is a pure data based point spread function modelling method, which uses calibration data from real observations or numerical simulated results as point spread function templates. According to real observation conditions, different levels of random noise or aberrations are added to point spread function templates, making them as realizations of the point spread function, i.e., simulated star images. Then we train the denoising autoencoder with realizations and templates of the point spread function. After training, the denoising autoencoder learns the manifold space of the point spread function and can map any star images obtained by wide field small aperture telescopes directly to its point spread function, which could be used to design data post-processing or optical system alignment methods.

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Precise measurement of the light curves for space debris with wide field of view telescope

Cited by 11 publications

References 29 publications

End-to-end space object detection method based on event camera

End-to-end space object detection method based on event camera

The Deep Neural Network based Photometry and Astrometry Framework for Wide Field Small Aperture Telescopes

Point spread function modelling for wide-field small-aperture telescopes with a denoising autoencoder

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