Efficient ALL vs. ALL collision risk analyses

Details Efficient ALL vs. ALL collision risk analyses Efficient ALL vs. ALL collision risk analyses

Sep 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011amos.confe..32e&link_type=abstract

Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, held in Wailea, Maui, Hawaii, September

Statistics

Computation

Scientific paper

In recent years, the space debris has gained a lot of attention due to the increasing amount of uncontrolled man-made objects orbiting the Earth. This population poses a significant and constantly growing thread to operational satellites. In order to face this thread in an independent manner, ESA has launched an initiative for the development of a European SSA System where GMV is participating via several activities. Apart from those activities financed by ESA, GMV has developed closeap, a tool for efficient conjunction assessment and collision probability prediction. ESÁs NAPEOS has been selected as computational engine and numerical propagator to be used in the tool, which can be considered as an add-on to the standard NAPEOS package. closeap makes use of the same orbit computation, conjunction assessment and collision risk algorithms implemented in CRASS, but at the same time both systems are completely independent. Moreover, the implementation in closeap has been validated against CRASS with excellent results. This paper describes the performance improvements implemented in closeap at algorithm level to ensure that the most time demanding scenarios (e.g., all catalogued objects are analysed against each other - all vs. all scenarios -) can be analysed in a reasonable amount of time with commercial-off-the-shelf hardware. However, the amount of space debris increases steadily due to the human activities. Thus, the number of objects involved in a full collision assessment is expected to increase notably and, consequently, the computational cost, which scales as the square of the number of objects, will increase as well. Additionally, orbit propagation algorithms that are computationally expensive might be needed to predict more accurately the trajectories of the space debris. In order to cope with such computational needs, the next natural step in the development of collision assessment tools is the use of parallelization techniques. In this paper we investigate the implementation of these techniques in the Smart Sieve filter. The computational memory requirements in an all vs. all scenario are low and thus, the OpenMP parallelization standard, which is specifically designed for shared memory architectures, seems to be an adequate choice. Apart from the computation of the orbits, the covariances of the different objects have to be computed at the time of closest approach of the detected conjunctions. So far, closeap implemented a numerical integrator to propagate the covariance of the objects. However, this approach has two important disadvantages. Firstly, it introduces an inconsistency as the SGP theory is used for the orbit computation while the covariance is numerically integrated. And secondly, the time needed for the numerical integration is also very significant. We analyse in this paper the use of SGP theory for the propagation of the covariance by means of numerical differentiation to overcome the previous issues.

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