A Geometrical-Statistical approach to outlier removal for TDOA measuments
This addresses outlier removal for TDOA measurements in fields such as sensor synchronization and remote sensing, but it is incremental as it builds on existing outlier removal procedures for specific scenarios.
The paper tackles the problem of outlier measurements in time differences of arrival (TDOA) or range differences (RD) data by proposing a statistically motivated outlier removal algorithm that works with only relative sensor positions. It validates the method through synthetic simulations and real experiments, showing it can reliably identify outliers across various fields like acoustic source localization and radar.
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.