Initial Orbit Determination using Short-Arc Angle and Angle Rate Data

DeMars, Kyle J.; Jah, Moriba; Schumacher, Paul W.

doi:10.1109/taes.2012.6237613

Cited by 91 publications

(20 citation statements)

References 5 publications

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“…• The threshold γ merging related to the statistical distance computed to assess the similarity between two tracks during the merging step (see Section IV.B) • The coefficients of the diagonal matrix D XX, 4 = diag(κ 1 , κ 2 , κ 3 , κ 4 ) reflecting the uncertainty induced by the assumption of circular orbits made in Section III.C.1 Table II lists the main parameter values retained for the simulations.…”

Section: A Setting Of the Algorithmmentioning

confidence: 99%

“…Following recent events, tracking small space debris for collision avoidance has become a topic of great interest [1][2][3][4]. The risk of collision in mid-Earth orbit and geosynchronous Earth orbit is of lesser concern to operators compared to the risk of collision in low earth orbit (LEO), which is higher because of a high debris density at this range of altitudes.…”

Section: Introductionmentioning

confidence: 99%

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Track initiation in low-earth-orbit objects using statistical modeling of sparse observations

Onera

Pannetier

Muller

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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In this paper, we investigate a new track initiation technique enabling the use of a low-cost radar system for low-Earth-orbit surveillance. This technique is based on a first association of observations with little ambiguity followed by a fast initial orbit determination. This study supports the feasibility of the system because this technique shows coverage of 84.4% within 6 d, with a combinatorial complexity kept under control when assessed in a realistic multitarget tracking context.

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Section: A Setting Of the Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Track initiation in low-earth-orbit objects using statistical modeling of sparse observations

Onera

Pannetier

Muller

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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“…Whereas most SSA literature treats the core elements of detection [1], tracking [2], characterization [3], and data association (usually within a tracking context) [4][5][6] independently from each other, the research approach advocated in this work is to seek an integrated approach to all of these problems. In treating detection, tracking, characterization, and data association separately, valuable information can be lost.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Object Space Surveillance Tracking Using Finite-Set Statistics

DeMars

Hussein

Frueh

et al. 2015

Journal of Guidance, Control, and Dynamics

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The dynamic tracking of objects is, in general, concerned with state estimation using imperfect data. Multiple object tracking adds the difficulty of encountering unknown associations between the collected data and the objects. State estimation of objects necessitates the prediction of uncertainty through nonlinear (in the general case) dynamical systems and the processing of nonlinear (in the general case) measurement data in order to provide corrections that refine the system uncertainty, where the uncertainty may be non-Gaussian in nature. The sensors, which provide the measurement data, are imperfect with possible misdetections, false alarms, and noise-affected data. The resulting measurements are inherently unassociated upon reception. In this paper, a Bayesian method for tracking an arbitrary, but known, number of objects is developed. The method is based on finite-set statistics coupled with finite mixture model representations of the multiobject probability density function. Instead of relying on first-moment approximations, such as the probability hypothesis density filter, to the full multiobject Bayesian posterior, as is often done for multiobject filtering, the proposed method operates directly on the exact Bayesian posterior. Results are presented for application of the method to the problem of tracking multiple space objects using synthetic line-of-sight data.

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“…Research related to the initial orbit determination of space objects using shortarc or sparse measurements from different observation sources is studied in depth (DeMars et al, 2012;Li et al, 2012;Ansalone & Curti, 2013;Bennett et al, 2014;Sang et al, 2014). To the orbit determination using GPS measurements, a large number of studies and effectual methods have been carried out in the past (Montenbruck et al, 2005;Bock et al, 2007;Choi et al, 2010;Gu & Yi, 2011;Chicharro et al, 2013;Wang Xin, 2013).…”

Section: ．．．．Introductionmentioning

confidence: 99%