Abstract
In this study, a novel approach based on the U-Net deep neural network for image segmentation is leveraged for real-time extraction of tracklets from optical acquisitions. As in all machine learning (ML) applications, a series of steps is required for a working pipeline: dataset creation, preprocessing, training, testing, and post-processing to refine the trained network output. Online websites usually lack ready-to-use datasets; thus, an in-house application artificially generates 360 labeled images. Particularly, this software tool produces synthetic night-sky shots of transiting objects over a specified location and the corresponding labels: dual-tone pictures with black backgrounds and white tracklets. Second, both images and labels are downscaled in resolution and normalized to accelerate the training phase. To assess the network performance, a set of both synthetic and real images was inputted. After the preprocessing phase, real images were fine-tuned for vignette reduction and background brightness uniformity. Additionally, they are down-converted to eight bits. Once the network outputs labels, post-processing identifies the centroid right ascension and declination of the object. The average processing time per real image is less than 1.2 s; bright tracklets are easily detected with a mean centroid angular error of 0.25 deg in 75% of test cases with a 2 deg field-of-view telescope. These results prove that an ML-based method can be considered a valid choice when dealing with trail reconstruction, leading to acceptable accuracy for a fast image processing pipeline.
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Abbreviations
- CCD:
-
Charged Coupled Device
- CNN:
-
Convolutional Neural Network
- DNN:
-
Deep Neural Network
- FCN:
-
Fully Connected Network
- FITS:
-
Flexible Image Transport System
- FoV:
-
Field of View
- GEO:
-
Geosynchronous Orbit
- ITG:
-
Image Tracklet Generator
- LEO:
-
Low-Earth Orbit
- PNG:
-
Portable Network Graphics
- SATCAT:
-
Satellite Catalogue
- SSA:
-
Space Situation Awareness
- SST:
-
Space Surveillance & Tracking
- TLE:
-
Two Line Elements
- Dice coeff :
-
Dice coefficient
- err:
-
Centroid error (pixel)
- f :
-
Interpolating elevation function (rad)
- r p2a :
-
Pixel to angle ratio (pixel/rad)
- ϸ :
-
Lagrangian basis function
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Acknowledgements
The authors would like to acknowledge the crucial support of the Italian Air Force with the invaluable material provided. The numerous images from their telescope observation campaign were key for an adequate algorithm testing phase.
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Funding note Open access funding provided by Politecnico di Milano within the CRUI-CARE Agreement.
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Andrea De Vittori, born in 1995 in Milan, obtained his M.Sc. degree in space engineering and his B.Sc. degree in mechanical engineering at Politecnico di Milano University. His master thesis was related to the application of AI techniques for space debris track reconstruction of LEO objects from optical and bistatic radar observation campaigns. From June 2020 to January 2021, Andrea worked as a research fellow at the same institution. Currently, he is a Ph.D. student in space engineering, focusing on the development of methods to compute the probability of collision and collision avoidance maneuver design algorithms for both impulsive and low-thrust maneuvers.
Riccardo Cipollone was born in 1995 in Teramo. He obtained an aerospace engineering B.Sc. degree and a space engineering M.Sc. degree with a thesis on machine learning-based techniques for optical and radar track reconstruction of LEO objects at Politecnico di Milano. After working as a research fellow at the same institution, he is currently a Ph.D. student in aerospace engineering. He is presently working on novel advanced correlation methods, spacecraft maneuver recognition, and optimal sensor tasking for space surveillance and tracking.
Pierluigi Di Lizia is an assistant professor of aerospace mechanics and spacecraft guidance and navigation at the Department of Aerospace Science and Technology of Politecnico di Milano. His main research areas include space surveillance and tracking, space situational awareness, guidance navigation, and control of proximity operations. He has 17 years of experience in projects involving research agencies and private companies. He has been co-author of over 160 papers (41 peer-reviewed journals). He is an associate editor of the COSPAR journal Advances in Space Research. He is a member of the Italian delegations to the Inter-Agency Space Debris Coordination Committee (IADC) and the Space Mission Planning Advisory Group (SMPAG). He is an Italian member of the CapTech Simulation of the European Defense Agency.
Mauro Massari is an associate professor at the Department of Aerospace Science and Technology of Politecnico di Milano, where he lectures on payload design in the space engineering M.Sc. program. He obtained his M.Sc. degree in aerospace engineering in 2000 from Politecnico di Milano and his Ph.D. degree in aerospace engineering in 2005 from Politecnico di Milano. His research interests are focused on the fields of numerical astrodynamics, spacecraft guidance navigation and control, uncertainty propagation, space surveillance and tracking, and space robotics.
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De Vittori, A., Cipollone, R., Di Lizia, P. et al. Real-time space object tracklet extraction from telescope survey images with machine learning. Astrodyn 6, 205–218 (2022). https://doi.org/10.1007/s42064-022-0134-4
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DOI: https://doi.org/10.1007/s42064-022-0134-4