Stochastic model-based discrimination of unresolved space objects at the sensor level

Sease, Brad; Black, Jonathan

doi:10.1109/acc.2016.7526586

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…The observation means sent into space to observe RSO can generate highly resolved images to calculate object signatures or to simulate the approach or landing phases. Spectral information provides valuable information for the analysis of light curves [10], [11] or for simulating the appearance of RSOs observed by spectro-imagers, from visible to infrared [12] [13]. Optical models of resolved objects are described in the literature, but present limitations.…”

Section: Previous Workmentioning

confidence: 99%

Hyperspectral optical modeling of resident space objects at high spatial resolution

Coiro,

Tricoli,

Margall

et al. 2024

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

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The proliferation of objects in space is a growing concern, and it is becoming crucial to have reliable information about these objects in order to be able to locate and identify them. The aim of this paper is to present SIRIUS (Simulateur tool dedicated to optical modeling of resident space objects (RSO) in order to design or configure ground-based or space-based future observation systems. This tool presents an innovative feature as it generates hyperspectral highly spatially resolved images, at low computation time. Images are physically realistic thanks to the use of a global illumination method implemented through optimized rendering methods on GPU. The optical signature of a RSO is calculated in the visible range, with a possible extension to infrared. The radiative environment consists of the Sun and the Earth (ground, and possibly atmosphere and clouds), and the reflectivity of the RSO is described by Spectral Bidirectional Reflectance Distribution Function (sBRDF) for each material with physical modelling of geometry wrinkling for MLI materials. The orbit, orientation and mobile parts rotation (solar panels of satellite) are taken into account dynamically over time. The result of the simulation consists of a series of spectral images at the sensor of turbulence and the sensor may be modeled using a Point Spread Function (PSF). In this paper, we propose a validation of the SIRIUS tool based on a comparison of a simulated light curve obtained from ENVISAT images taken with the MeO telescope at the OCA (Côte d'Azur Observatory, France) during 2019, and an evaluation of the MLI wrinkling on the signature. The comparison between the simulated and experimental data of this uncontrolled satellite highlights the performance of this new predictive tool.

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Section: Previous Workmentioning

confidence: 99%

Hyperspectral optical modeling of resident space objects at high spatial resolution

Coiro,

Tricoli,

Margall

et al. 2024

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

View full text Add to dashboard Cite

show abstract

“…As early as 2012, groups have probed the use photometric measurements to exploit the underlying spectral signatures of satellites. 8 This work has continued over the last decade, with small datasets on targets of opportunity, [9][10][11][12][13] with large datasets of single-band photometry, 14,15 and on the suitability of filter sets to SDA. 16 After a decade of work, no technology has emerged to generally approach the problem of quantitative SDA.…”

Section: Filter Photometry For Sdamentioning

confidence: 99%

The potential for satellite recognition from ground-based filter photometry

Gazak,

Phelps,

Swindle

et al. 2023

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX

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Recent work demonstrates recognition of artificial satellites in spatially unresolved observations by utilizing learned spectroscopic classification (SpectraNet 1 ). That proof of concept exposes critical identifying information currently lacking in catalogs used by space domain awareness stakeholders. In this work we present experiments to increase the accessibility and efficiency of SpectraNet enabled systems by probing the bandpass and resolution requirements for learned recognition of satellites. To enable affordable, off the shelf instrumentation, this work focuses on wavelength ranges accessible by Silicon-based detectors (400-1000 nanometers). While the SpectraNet proof of concept utilized a medium resolution spectrograph on a 3.6 meter telescope at 10,000 feet elevation, we show that the identifying spectral features relate to an object's overall spectral energy density and are accessible at significantly lower spectral resolution. This finding relaxes the need for large telescopes at high altitude. We further demonstrate that the technology can be utilized via simultaneous multi-band filter photometry. Design considerations for properly obtaining simultaneous photometry are discussed. Thus this work demonstrates that−in simulation−learned spectral recognition is an effective technology from high resolution spectrographs through simultaneous multi-filter photometric instruments. We provide experiments to understand the minimum engineered system needed to perform effective learned recognition, such that the technology can be hardened and widely proliferated.

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“…The applications of photometry in Space Domain Awareness (SDA) include stability analysis, 1 shape recovery, 2 and size and materials studies. 3,4 Proper measurement of the input photometric data into these tools determines the quality of output information. Typically, such studies involve manual collection, reduction, and analysis of small sets of data on one or few targets.…”

Section: Introductionmentioning

confidence: 99%

Leveraging synthetic data for star and satellite photometry

Chang,

Cabello,

Houchard

et al. 2024

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II

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Aperture photometry is a critical method for estimating the visual magnitudes of stars and satellites, essential in Space Domain Awareness (SDA) for tasks like collision avoidance. Traditional methods have fixed aperture shapes, limiting accuracy and adaptability. We introduce a novel approach that defines pixel-specific regions for the aperture and annulus, significantly improving accuracy. Nevertheless, conventional aperture photometry is constrained by predefined equations, leading to errors and sensitivity to image conditions. To overcome these limitations, we propose a learned photometry pipeline that combines aperture photometry with machine learning. Our approach demonstrates remarkable effectiveness for both stars and satellites across diverse image conditions. We rigorously tested it on three datasets, including a custom synthetic dataset and real imagery. Our results showcase outstanding performance, with a 1.44% error in star visual magnitude estimation and a 0.64% error in satellite visual magnitude estimation.

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Stochastic model-based discrimination of unresolved space objects at the sensor level

Cited by 3 publications

References 5 publications

Hyperspectral optical modeling of resident space objects at high spatial resolution

Hyperspectral optical modeling of resident space objects at high spatial resolution

The potential for satellite recognition from ground-based filter photometry

Leveraging synthetic data for star and satellite photometry

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