A Geometrical-Statistical approach to outlier removal for TDOA measuments

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.

30 pages, 10 figure, 3 tables, in press on IEEE Transactions on Signal Processing

References in corpus (3)

• TDOA Matrices: Algebraic Properties and their Application to Robust Denoising with Missing Data
• Source localization and denoising: a perspective from the TDOA space
• The algebro-geometric study of range maps

Cited by in corpus (3)

• Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates
• Deep Pattern of Time Series and Its Applications in Estimation, Forecasting, Fault Diagnosis and Target Tracking
• Neurodynamic TDOA localization with NLOS mitigation via maximum correntropy criterion