Hierarchical Time-of-Arrival Self-Calibration using Robust Receiver Distance Averaging

This paper suggests an approach to solving the time-of-arrival self-calibration problem for imbalanced problems, where there is a low number of receivers but many senders. The idea is to solve several subproblems created by only using data relating to four receivers. The solutions are then clustered according to a proposed metric in order to remove failed reconstructions. They are then combined into a solution for the total system. This approach is compared to state-of-the-art methods and shown to be robust against both missing data and outliers. It can also handle a large number of sender positions.