Low-rank matrix completion is the problem where one tries to recover a low-rank matrix from
noisy observations of a subset of its entries. In this paper, we propose RMC, a new method …

This paper presents a novel algorithm for the automatic 3D localization of a set of sensors in
an unknown environment. Given the measures of a set of time of arrival delays at each …

This paper addresses the Sensor Network Localization (SNL) problem using received signal
strength. The SNL is formulated as an Euclidean Distance Matrix Completion (EDMC) …

It was recently shown that low rank Matrix Completion (MC) theory can support the design of
new sampling schemes in the context of MIMO radars, enabling significant reduction of the …

Mobile nodes with sensing, computing and communicating capabilities are usually deployed
in indoor environments as wireless sensor networks (WSN) for internet-of-things …

DOUGLAS–RACHFORD FEASIBILITY METHODS FOR MATRIX COMPLETION
PROBLEMS Page 1 ANZIAM J. 55(2014), 299–326 doi:10.1017/S1446181114000145 …

Sensor localization from only connectivity information is a highly challenging problem. To
this end, our result for the first time establishes an analytic bound on the performance of the …

Localization in terrestrial and non-terrestrial networks plays an important role in various
applications, such as autonomous driving, robotics, and unmanned aerial vehicles. Although …

Collaborative filtering is a novel statistical technique to obtain useful information or to make
predictions based on data from multiple agents. A large number of such datasets are …

Euclidean distance matrices (EDMs) are central players in many diverse fields including
psychometrics, NMR spectroscopy, machine learning and sensor networks. However, they …