In this paper we study the problem of estimating receiver and sender positions from time-difference-of-arrival measurements, assuming an unknown constant time-difference-of-arrival offset. This problem is relevant for example for repetitive sound events. In this paper it is shown that there are three minimal cases to the problem. One of these (the five receiver, five sender problem) is of particular importance. A fast solver (with run-time under 4 µs) is given. We show how this solver can be used in robust estimation algorithms, based on RANSAC, for obtaining an initial estimate followed by local optimization using a robust error norm. The system is verified on both real and synthetic data.

Indoor localization and navigation is a much researched and difficult problem. The best solutions, usually use expensive specialized equipment and/or prior calibration of some form. To the average person with smart or Internet-Of-Things devices, these solutions are not feasible, particularly in large scales. With hardware advancements making Ultra-Wideband devices more accurate and low powered, this unlocks the potential of having such devices in commonplace around factories and homes, enabling an alternative method of navigation. Therefore, indoor anchor calibration becomes a key problem in order to implement these devices efficiently and effectively. In this paper, we present a method to fuse radio SLAM (also known as Time-Of-Arrival self-calibration) maps together in a linear way. In doing so we are then able to collaboratively calibrate the anchor positions in 3D to native precision of the devices. Furthermore, we introduce an automatic scheme to determine which of the maps are best to use to further improve the anchor calibration and its robustness but also show which maps could be discarded. Additionally, when a map is fused in a linear way, it is a very computationally cheap process and produces a reasonable map which is required to push for crowd-sourced data acquisition.

Indoor localisation is a currently a key issue, from robotics to the Internet of Things. With hardware advancements making Ultra-Wideband devices more accurate and low powered (potentially even passive), this unlocks the potential of having such devices in common place around factories and homes, enabling an alternative method of navigation. Therefore, anchor calibration indoors becomes a key problem in order to implement these devices efficiently and effectively. In this paper, we study the possibility for sequentially gathering Ultra-Wideband Time-of-Arrival measurements and using previously studied robust solvers, merge solutions together in order to calculate anchor positions in 3D in real-time. Here it is assumed that there is no prior knowledge of the anchor positions. This is then validated using Ultra-Wideband Time-of-Arrival data gathered by a Bitcraze Crazyflie quadcopter in 2D motion, 3D motion and full flight.

This paper presents a phase-based positioning framework using a massive MIMO system. The phase-based distance estimates of MPCs together with other parameters are tracked with an EKF, the state dimension of which varies with the birth-death processes of paths. The RIMAX and the modeling of dense multipath components in the framework further enhance the quality of parameter tracking by providing an accurate initial state and the underlying noise covariance. The tracked MPCs are fed into a time-of-arrival self-calibration positioning algorithm for simultaneous trajectory and environment estimation. Throughout the positioning process, no prior knowledge of the surrounding environment and base station position is needed. The performance is demonstrated with the measurement of a 2D complex movement, which was performed in a sports hall with an antenna array with 128 ports as base station using a standard cellular bandwidth of 40 MHz. The positioning result shows that the mean deviation of the estimated user equipment trajectory from the ground truth is 13 cm. In summary, the proposed framework is a promising high-resolution radio-based positioning solution for current and next generation cellular systems.

In this paper we present a system for performing low rank matrix factorization. Low-rank matrix factorization is an essential problem in many areas, including computer vision with applications in affine structure-from-motion, photometric stereo, and non-rigid structure from motion. We specifically target structured data patterns, with outliers and large amounts of missing data. Using recently developed characterizations of minimal solutions to matrix factorization problems with missing data, we show how these can be used as building blocks in a hierarchical system that performs bootstrapping on all levels. This gives a robust and fast system, with state-of-the-art performance.