Confidence region of least squares solution for single-arc observations

Principe, G, Armellin, R and Lewis, H (2016) Confidence region of least squares solution for single-arc observations In: Advanced Maui Optical and Space Surveillance Technologies Conference, 2016-09-20 - 2016-09-23, Maui County, Hawaii, USA.

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Abstract

The total number of active satellites, rocket bodies, and debris larger than 10 cm is currently about 20,000. Considering all resident space objects larger than 1 cm this rises to an estimated minimum of 500,000 objects. Latest generation sensor networks will be able to detect small-size objects, producing millions of observations per day. Due to observability constraints it is likely that long gaps between observations will occur for small objects. This requires to determine the space object (SO) orbit and to accurately describe the associated uncertainty when observations are acquired on a single arc. The aim of this work is to revisit the classical least squares method taking advantage of the high order Taylor expansions enabled by differential algebra. In particular, the high order expansion of the residuals with respect to the state is used to implement an arbitrary order least squares solver, avoiding the typical approximations of differential correction methods. In addition, the same expansions are used to accurately characterize the confidence region of the solution, going beyond the classical Gaussian distributions. The properties and performances of the proposed method are discussed using optical observations of objects in LEO, HEO, and GEO.

Item Type:	Conference or Workshop Item (Conference Paper)
Subjects :	Electronic Engineering
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Principe, G Armellin, R Lewis, H
Date :	20 September 2016
Copyright Disclaimer :	Copyright © 2016 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS) – www.amostech.com
Contributors :	Contribution Name Email ORCID http://www.loc.gov/loc.terms/relators/PBL AMOS,
Related URLs :	Publisher
Depositing User :	Symplectic Elements
Date Deposited :	07 Feb 2017 15:47
Last Modified :	31 Oct 2017 19:06
URI:	http://epubs.surrey.ac.uk/id/eprint/813475

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