Detection Of Unknown Space Objects Based On Optimal Sensor Tasking And Hypothesis Surfaces Using Variational Equations
With the rapid increase in the number of space missions, the hazard of growing space debris population in important space territories like the geosynchronous Earth orbit (GEO) region deserves deeper scrutiny. One of the main challenges is to find optimal sensor tasking schemes to detect objects that are not previously cataloged. This paper introduces a method in which a so-called hypothesis surface is used to indicate valuable sensor viewing directions. The hypothesis surface is created using analytic orbit perturbation theory with crucial additions to capture resonance effects and offer sufficient accuracy for the near-Earth region. So-called hypothesis objects are analytically propagated from an early epoch to an epoch near the observation session. Their average positions over the next 24 h are used to design the hypothesis surface. Two cases are shown: (i) a validation case, where we use the hypothesis surface for detecting GEO objects currently in the two-line element set (TLE) catalog, and (ii) a survey strategy to detect high area-to-mass ratio (HAMR) objects. For a variety of observation strategies, for the validation case, we demonstrate that the hypothesis surface is successfully able to detect a large number of GEO objects.
S. N. Paul et al., "Detection Of Unknown Space Objects Based On Optimal Sensor Tasking And Hypothesis Surfaces Using Variational Equations," Journal of the Astronautical Sciences, vol. 69, no. 4, pp. 1179 - 1215, Springer; American Astronautical Society, Aug 2022.
The definitive version is available at https://doi.org/10.1007/s40295-022-00333-z
Mechanical and Aerospace Engineering
Keywords and Phrases
Analytic orbit theory; Sensor tasking; Survey strategies; Uncertainty propagation
International Standard Serial Number (ISSN)
Article - Journal
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01 Aug 2022