Extracting GEO orbit populations from optical surveys

Matney, Mark; Stansbery, Eileen K.; Africano, J. L.; Jarvis, K. S.; Jorgensen, K.; Thumm, Tracy

doi:10.1016/j.asr.2003.11.014

Cited by 10 publications

(2 citation statements)

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“…This 32 cm aperture Schmidt telescope had a 1.7° x 1.7° field-ofview (FOV), and operated for several years, beginning in 1998. This was also the first data used to statistical orbit distributions of the GEO environment 7 .…”

Section: Measurementsmentioning

confidence: 99%

Calculating Statistical Orbit Distributions Using GEO Optical Observations with the Michigan Orbital Debris Survey Telescope (MODEST)

Matney

Barker

Seitzer

et al. 2006

57th International Astronautical Congress

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NASA's Orbital Debris measurements program has a goal to characterize the small debris environment in the geosynchronous orbit (GEO) region using optical telescopes ("small" refers to objects too small to catalog and track with current operational systems). Traditionally, observations of GEO and near-GEO objects involve following the object with the telescope long enough to obtain an orbit suitable for tracking purposes. Telescopes operating in survey mode, however, randomly observe objects that pass through their field-of-view. Typically, these short-arc observation are inadequate to obtain detailed orbits, but can be used to estimate approximate circular orbit elements (semi-major axis, inclination, and ascending node). From this information, it should be possible to make statistical inferences about the orbital distributions of the GEO population bright enough to be observed by the system. The Michigan Orbital Debris Survey Telescope (MODEST) has been making such statistical surveys of the GEO region for five years. During that time, the telescope has made sufficient observations in enough areas of the GEO belt to have achieved nearly complete coverage. That means that almost all objects in all possible orbits in the GEO and near-GEO region had a non-zero chance of being observed. Some regions (such as those near zero inclination) have had good coverage, while others are poorly covered. Nevertheless, it is possible to remove these statistical biases and reconstruct the orbit populations within the limits of sampling error. In this paper, these statistical techniques and assumptions are described, and the techniques are applied to the current MODEST data set to arrive at our best estimate of the GEO orbit population distribution.

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Section: Measurementsmentioning

confidence: 99%

Calculating Statistical Orbit Distributions Using GEO Optical Observations with the Michigan Orbital Debris Survey Telescope (MODEST)

Matney

Barker

Seitzer

et al. 2006

57th International Astronautical Congress

View full text Add to dashboard Cite

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“…While space debris is illuminated by the Sun, the reflected light can be collected during night and the astrometric and photometric result can be obtained by the measurements performed on CCD images. While the former is used for orbit determination and object catalog (Matney et al 2004b), the latter allows the estimation of the shape and rotational state of the objects (Yanagisawa & Kurosaki 2012), which is expected to be necessary for active space debris removal in the future.…”

Section: Introductionmentioning

confidence: 99%

Image Restoration Based on Statistic PSF Modeling for Improving the Astrometry of Space Debris

Sun

Jia

2017

PASP

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Space debris is a special kind of fast-moving, near-Earth objects, and it is also considered to be an interesting topic in time-domain astronomy. Optical survey is the main technique for observing space debris, which contributes much to the studies of space environment. However, due to the motion of registered objects, image degradation is critical in optical space debris observations, as it affects the efficiency of data reduction and lowers the precision of astrometry. Therefore, the image restoration in the form of deconvolution can be applied to improve the data quality and reduction accuracy. To promote the image processing and optimize the reduction, the image degradation across the field of view is modeled statistically with principal component analysis and the efficient mean point-spread function (PSF) is derived from raw images, which is further used in the image restoration. To test the efficiency and reliability, trial observations were made for both low-Earth orbital and high-Earth orbital objects. The positions of all targets were measured using our novel approach and compared with the reference positions. The performance of image restoration employing our estimated PSF was compared with several competitive approaches. The proposed image restoration outperformed the others so that the influence of image degradation was distinctly reduced, which resulted in a higher signal-to-noise ratio and more precise astrometric measurements.

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