@conference {812, title = {Distributed AoA-based Source Positioning in NLOS with Sensor Networks}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2015}, month = {04/2015}, publisher = {IEEE}, organization = {IEEE}, address = {Brisbane, Australia}, abstract = {
This paper focuses on the problem of positioning a source using angle-of-arrival measurements taken by a wireless sensor network in which some of the nodes experience non-line-of-sight (NLOS) propagation conditions. In order to mitigate the errors induced by the nodes in NLOS, we derive an algorithm that combines the expectation-maximization algorithm with a weighted least-squares estimation of the source position so that the nodes in NLOS are eventually identified and discarded. Moreover, a distributed version of this algorithm based on a diffusion strategy that iteratively refines the position estimate while driving the network to a consensus is presented.
}, keywords = {compass, wsn}, author = {Pere Gimenez-Febrer and Alba Pag{\`e}s-Zamora and Silvana Silva Pereira and R. L{\'o}pez-Valcarce} } @conference {811, title = {Distributed TLS Estimation under Random Data Faults}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2015}, month = {04/2015}, publisher = {IEEE}, organization = {IEEE}, address = {Brisbane, Australia}, abstract = {
This paper addresses the problem of distributed estimation of a parameter vector in the presence of noisy input and output data as well as data faults, performed by a wireless sensor network in which only local interactions among the nodes are allowed. In the presence of unreliable observations, standard estimators become biased and perform poorly in low signal-to-noise ratios. We propose two different distributed approaches based on the Expectation-Maximization algorithm: in the first one the regressors are estimated at each iteration,
whereas the second one does not require explicit regressor estimation. Numerical results show that the proposed methods approach the performance of a clairvoyant scheme with knowledge of the random data faults.
}, keywords = {compass, wsn}, author = {Silvana Silva Pereira and Alba Pag{\`e}s-Zamora and R. L{\'o}pez-Valcarce} } @conference {733, title = {How to Implement Doubly-Stochastic Matrices for Consensus-Based Distributed Algorithms}, booktitle = {IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)}, year = {2014}, address = {A Coru{\~n}a, Spain}, abstract = {
Doubly-stochastic matrices are usually required by
consensus-based distributed algorithms. We propose a simple
and efficient protocol and present some guidelines for implementing
doubly-stochastic combination matrices even in noisy,
asynchronous and changing topology scenarios. The proposed
ideas are validated with the deployment of a wireless sensor
network, in which nodes run a distributed algorithm for robust
estimation in the presence of nodes with faulty sensors.
}, keywords = {compass, dynacs, wsn}, doi = {10.1109/SAM.2014.6882409}, author = {S. Valc{\'a}rcel-Macua and C. Moreno-Le{\'o}n and J. S. Romero and Silvana Silva Pereira and Javier Zazo and Alba Pag{\`e}s-Zamora and R. L{\'o}pez-Valcarce and S. Zazo} }