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\title{
Parametric Analysis of
Antares Re-Entry Guidance
Algorithms using Advanced
Test Generation and Data
Analysis%
%\thanks{The research described in this paper
%was carried out at the NASA-Ames Research Center and
%West Virginia University under
%contracts with NASA.
%Reference herein to any specific commercial product, process, or service
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\begin{abstract}
The any-time lunar return requirement for 
the Constellation "Orion" capsule drives algorithm 
development of skip re-entry guidance 
algorithms as well as hardware items such 
as configuration and sizing of the reaction 
control system.  A skip re-entry maneuver 
involves an initial aero-braking maneuver 
and then a skip out of the earth's 
atmosphere. At apogee of the skip 
maneuver, control is applied such that the 
capsule de-orbits and descends to a 
targeted landing zone.  For a successful 
landing, a significant number of 
requirements must be fulfilled (e.g, 
acceptable G-loads, fuel consumption and 
on-target landing). 

The performance of the capsule depends on 
a large number of parameters and a major 
task is to establish safe ranges for those parameters. 
Exhaustive exploration of all parameter combinations
is infeasible for these complex systems, so parameters are randomly
sampled from a defined distribution for a statisticaly significant
number of runs (traditional Monte Carlo testing).  Vast amounts of
data can be generated, and manual inspection of this data is usually
confined to gross features of the solution (such as absolute compliance
with requirements).  Anomolous data can easily be overlooked in this situation.

We have addressed this set of problems by applying a combination of technologies
to the area of parametric test.  We utilize n-factor (2,3) 
combinatorial parameter variations to limit the number of cases, but still 
explore important interactions in the parameter space in a systematic fashion.
The data generated is automatically analyzed through a combination of
undirected learning using a Bayesian multivariate clustering technique
(AutoBayes) and directed learning of critical parameter ranges using
the machine-learning tool "tar3", a treatment learner.
Covariance analysis with scatter plots and likelihood contours are 
used to visualize correlation between simulation parameters and simulation 
results, a task that requires tool support, especially for large and complex models. 
We present results of simulation experiments that have been carried out 
with Antares 7.2.0, the Orion simulation model.

\end{abstract}

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