Associating optical measurements and estimating orbits of geocentric objects through population-based meta-heuristic methods

Paper number

IAC-15,A6,9,10,x29692

Author

Mr. Michiel Zittersteijn, Astronomical Institute University of Bern (AIUB), Switzerland

Coauthor

Dr. Alessandro Vananti, Astronomical Institute University of Bern (AIUB), Switzerland

Coauthor

Prof. Thomas Schildknecht, Astronomical Institute University of Bern (AIUB) / SwissSpace Association, Switzerland

Coauthor

Mr. Juan Carlos Dolado Perez, Centre National d'Etudes Spatiales (CNES), France

Coauthor

Mr. Vincent Martinot, Thales Alenia Space France, France

Year

2015

Abstract

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical
means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this
context both the correct associations among the observations and the orbits of the objects have to be
determined. The complexity of the MTT problem is defined by its dimension $S$. Current research tends
to focus on the $S=2$ MTT problem. The reason for this is that for $S=2$ the problem has a P-complexity,
when $S\geq 3$ the problem becomes NP-hard. However, with $S=2$ the decision to associate a set of
observations is based on the minimum amount of information, in ambiguous situations (e.g. satellite
clusters) this will lead to incorrect associations. The $S \geq 3$ MTT problem is an NP-hard combinatorial
optimization problem. There are two general ways to solve this. One way is to seek the optimum
solution, this can be achieved by applying a branch-and-bound algorithm. When using these
algorithms the problem has to be greatly simplified to keep the computational cost at a reasonable
level. Another option is to approximate the solution by using meta-heuristic methods. These methods
aim to efficiently explore the different possible combinations so that a reasonable result can be
obtained with a reasonable computational effort. To this end several population-based meta-heuristic
methods are implemented and tested on simulated optical measurements. The algorithms are compared
both in their performance and in their time complexity. With the advent of improved sensors and a
heightened interest in the problem of space debris, it is expected that the number of tracked objects
will grow by an order of magnitude in the near future. This research aims to provide a method that can
treat the correlation and orbit determination problems simultaneously, and is able to efficiently process
large data sets with minimal manual intervention.

Abstract document

IAC-15,A6,9,10,x29692.brief.pdf

Manuscript document

IAC-15,A6,9,10,x29692.pdf (🔒 authorized access only).

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