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.

Compagnoni, M., Pini, A., Canclini, A., Bestagini, P., Antonacci, F., Tubaro, S., Sarti, A., A Geometrical-Statistical Approach to Outlier Removal for TDOA Measurements, <<IEEE TRANSACTIONS ON SIGNAL PROCESSING>>, 2017; 65 (15): 3960-3975. [doi:10.1109/TSP.2017.2701311] [http://hdl.handle.net/10807/119537]

A Geometrical-Statistical Approach to Outlier Removal for TDOA Measurements

Pini, Alessia
Methodology
;
2017

Abstract

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.
2017
Inglese
Compagnoni, M., Pini, A., Canclini, A., Bestagini, P., Antonacci, F., Tubaro, S., Sarti, A., A Geometrical-Statistical Approach to Outlier Removal for TDOA Measurements, <<IEEE TRANSACTIONS ON SIGNAL PROCESSING>>, 2017; 65 (15): 3960-3975. [doi:10.1109/TSP.2017.2701311] [http://hdl.handle.net/10807/119537]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/119537
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