Fast and Robust Stratified Self-Calibration Using Time-Difference-Of-Arrival Measurements

Abstract

In this paper we study the problem of estimating receiver and sender positions using time-difference-of-arrival measurements. For this, we use a stratified, two-tiered approach. In the first step the problem is converted to a low-rank matrix estimation problem. We present new, efficient solvers for the minimal problems of this low-rank problem. These solvers are used in a hypothesis and test manner to efficiently remove outliers and find an initial estimate which is used for the subsequent step. Once a promising solution is obtained for a sufficiently large subset of the receivers and senders, the solution can be extended to the remaining receivers and senders. These steps are then combined with robust local optimization using the initial inlier set and the initial estimate as a starting point. The proposed system is verified on both real and synthetic data.

GCC-PHAT Re-Imagined - A U-Net Filter for Audio TDOA Peak-Selection

“…The experiment is driven by a Python 3 implementation, code available 3 . The code was developed iteratively and ad-hoc, as a proof-of-concept.…”

“…[2], is the first signal processing step. The state-of-the-art method is the GCC-PHAT method and variants thereof, [3]. It has been observed in previous papers that the potential for improvement is large here.…”