Statistics – Computation
Scientific paper
Oct 2008
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008adspr..42.1352g&link_type=abstract
Advances in Space Research, Volume 42, Issue 8, p. 1352-1363.
Statistics
Computation
Scientific paper
The design of a low-cost spacecraft to impact on a small, faint Near Earth Object (NEO), poses major challenges. This paper focuses on the terminal phase of such impact mission, analyzing the capability of autonomous Guidance, Navigation and Control (GNC) systems to compensate the deviations in the impact point to achieve a successful collision. The autonomous GNC system employs the information of the optical sensors to estimate the parameters allowing the computation of divert maneuvers to achieve the impact. GMV has developed a simulator, with different levels of sophistication, and a set of different GNC algorithms to help in the design process. This tool is used for different purposes such as: dimensioning the sensors and actuators, verifying mission requirements, computing figures of merit of different SC configurations and evaluating GNC performances. Four GNC algorithms are compared: low-thrust proportional navigation using a fading memory filter, high-thrust predictive guidance using either a Kalman filter or a batch-sequential least-squares filter, and a mid-thrust hybrid predictive-proportional guidance using a fading memory filter. Monte Carlo analysis using global-performances models of the optical sensors for each of these GNC algorithms are presented for two different asteroids (1989 ML and 2002 AT4), showing the mission parameters driving the mission performances. In addition, single-runs with high-fidelity optical sensors models are presented to validate the Monte Carlo simulations.
Corral Carlos
Gil-Fernández Jesús
Panzeca R.
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