  This paper presents an algorithm for the preprocessing of observation data
aimed at improving the robustness of orbit determination tools. Two objectives
are fulfilled: obtain a refined solution to the initial orbit determination
problem and detect possible outliers in the processed measurements. The
uncertainty on the initial estimate is propagated forward in time and
progressively reduced by exploiting sensor data available in said propagation
window. Differential algebra techniques and a novel automatic domain splitting
algorithm for second-order Taylor expansions are used to efficiently propagate
uncertainties over time. A multifidelity approach is employed to minimize the
computational effort while retaining the accuracy of the propagated estimate.
At each observation epoch, a polynomial map is obtained by projecting the
propagated states onto the observable space. Domains that do no overlap with
the actual measurement are pruned thus reducing the uncertainty to be further
propagated. Measurement outliers are also detected in this step. The refined
estimate and retained observations are then used to improve the robustness of
batch orbit determination tools. The effectiveness of the algorithm is
demonstrated for a geostationary transfer orbit object using synthetic and real
observation data from the TAROT network.
