A Geometrical-Statistical approach to outlier removal for TDOA measuments

TL;DR

This paper introduces a geometrical-statistical outlier removal algorithm for TDOA measurements that is versatile, application-independent, and validated through simulations and real-world experiments.

Contribution

It presents a novel outlier removal method based on TDOA-space formalism that only requires relative sensor positions, applicable across various fields.

Findings

Effective outlier detection demonstrated in simulations

Successful validation with real experimental data

Applicable to multiple TDOA-based applications

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.