Speaker
Mr
Mirco Rasotto
(Dinamica Srl)
Description
From 2004 up to the date more than 200 launch vehicles operated by five independent nations and two international organizations placed satellites in Geostationary Earth Orbit (GEO). In almost all cases, each successful launch left one or more pieces of debris in Geostationary Transfer Orbits (GTO). Particularly, many of this space debris consist of large spent upper stages of launch vehicles whose atmosphere re-entry might violate the constraint on casualty risk of 1/10000: as of 16 October 2014, it is expected that about 79 spent upper stages operating on GTOs with an inclination lower than 20 degree will enter the Earth atmosphere in the next 200 years. Moreover, the GTOs are highly eccentric orbits with perigee normally at low altitudes (170–650 km) and the apogee near geo-stationary altitude (35,780 km). Thus, space debris in GTOs generally passes through densely populated regions such as Low Earth Orbit (LEO) and GEO regions, being a hazard for the safety of other operating spacecraft. In light of the above, the improvement of re-entry prediction of GTO spent upper stages is a key issue to manage both on-orbit collision risk and on-ground casualty risk.
Currently the only public data source available for re-entry prediction of a space object are represented by Two Line Elements (TLEs), provided by the United States Strategic Command (USSTRATCOM). However, this set of data are inaccurate and do not come with uncertainty information, making their use in re-entry prediction and conjunction analyses challenging, especially for the GTO space object. This leads to the need of using the observational data to improve the re-entry prediction.
The design of observation strategy for GTO upper stage is not trivial. The detection and tracking of space objects on GTOs might require more than a single sensor in fact, since the distance from the observer has large variation along the orbit; this multiple sensors configuration might involve problems such scheduling or data fusion, making space object observation complex and costly. In addition, design of an optimal observation strategy for improvement of re-entry prediction involves the definition of a high-accuracy orbit determination (OD) algorithm, and the implementation of proper methods for uncertainty mapping. This might require the definition of accurate dynamical models in order to describe the effects of third-body perturbations and the Earth’s oblateness and to capture the intricacies of re-entry phase, as well as the use of nonlinear technique for orbit determination.
In this paper, a systematic approach to design the observation strategy of spent upper stage moving on GTOs is presented. More specifically, the design is formulated as a multi-objective optimization problem solved by means of a multi-objective genetic algorithm (MGA). This approach allows minimizing both the number of total measurements required to detect the space object and the error on re-entry prediction. Within the optimization process a nonlinear OD algorithm is run to determine the estimates of both initial state and model parameters. The Nonlinear Least Square Filter (NLSF) technique is implemented, exploiting the differential algebra framework to reduce the computational effort related to OD problem solution. Finally, the software tool IRIS is developed to accurately simulate the observation campaigns based on geometry and constraints of existing sensors currently available to European Space Agency (ESA).
| Applicant type | First author |
|---|
Primary author
Dr
Giuseppe Di Mauro
(Dinamica SrL)
Co-authors
Dr
Mauro Massari
(Politecnico di Milano)
Mr
Mirco Rasotto
(Dinamica Srl)
Dr
Pierluigi Di Lizia
(Politecnico di Milano)
Mr
Quirin Funke
(IMS SPACE CONSULTANCY GMBH c/o ESA-ESOC)
Dr
Roberto Armellin
(University of Southampton, Faculty of Engineering and the Environment)
Dr
Tim Flohrer
(ESA-ESOC, Space Debris Office)
