  The curse of outlier measurements in estimation problems is a well known
issue in a variety of fields. Therefore, outlier removal procedures, which
enables the identification of spurious measurements within a set, have been
developed for many different scenarios and applications. In this paper, we
propose a statistically motivated outlier removal algorithm for time
differences of arrival (TDOAs), or equivalently range differences (RD),
acquired at sensor arrays. The method exploits the TDOA-space formalism and
works by only knowing relative sensor positions. As the proposed method is
completely independent from the application for which measurements are used, it
can be reliably used to identify outliers within a set of TDOA/RD measurements
in different fields (e.g. acoustic source localization, sensor synchronization,
radar, remote sensing, etc.). The proposed outlier removal algorithm is
validated by means of synthetic simulations and real experiments.
