Localization in wireless sensor networks (WSNs) is regarded as an emerging technology for
numerous cyber-physical system applications, which equips wireless sensors with the …

This paper gives an overview of the majorization-minimization (MM) algorithmic framework,
which can provide guidance in deriving problem-driven algorithms with low computational …

We propose a class of convex relaxations to solve the sensor network localization problem,
based on a maximum likelihood (ML) formulation. This class, as well as the tightness of the …

Recent years have witnessed a surge of interest in parallel and distributed optimization
methods for large-scale systems. In particular, nonconvex large-scale optimization problems …

This paper revisits the problem of locating a signal-emitting source from time-difference-of-
arrival (TDOA) measurements under non-line-of-sight (NLOS) propagation. Many currently …

We address the sensor network localization problem given noisy range measurements
between pairs of nodes. We approach the nonconvex maximum-likelihood formulation via a …

Methods for electro-or magnetoencephalography (EEG/MEG) based brain source imaging
(BSI) using sparse Bayesian learning (SBL) have been demonstrated to achieve excellent …

Time-difference-of-arrival (TDOA) techniques are widely used in high-accuracy positioning
systems. The weighted nonlinear least squares (NLLS) algorithm can be used in such …

In this paper, we formulate Gaussian process regression with observations under the
localization uncertainty due to the resource-constrained sensor networks. In our formulation …

The successful deployment and operation of location-aware networks, which have recently
found many applications, depends crucially on the accurate localization of the nodes …