Robust time-of-arrival self calibration with missing data and outliers

Abstract

The problem of estimating receiver-sender node positions from measured receiver-sender distances is a key issue in different applications such as microphone array calibration, radio antenna array calibration, mapping and positioning using ultra-wideband and mapping and positioning using round-trip-time measurements between mobile phones and Wi-Fi-units. Thanks to recent research in this area we have an increased understanding of the geometry of this problem. In this paper, we study the problem of missing information and the presence of outliers in the data. We propose a novel hypothesis and test framework that efficiently finds initial estimates of the unknown parameters and combine such methods with optimization techniques to obtain accurate and robust systems. The proposed systems are evaluated against current state-of-the-art methods on a large set of benchmark tests. This is evaluated further on Wi-Fi round-trip time and ultra-wideband measurements to give a realistic example of self calibration for indoor localization.