Robust Time-of-Arrival Self Calibration with Missing Data and Outliers

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 ultrawideband
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 roundtrip
time and ultra-wideband measurements to give a realistic
example of self calibration for indoor localization.