We consider the problem of estimating the coefficients in a multivariable linear model by means of a wireless sensor network

which may be affected by anomalous measurements. The noise covariance matrices at the different sensors are assumed

unknown. Treating outlying samples, and their support, as additional nuisance parameters, the Maximum Likelihood

estimate is investigated, with the number of outliers being estimated according to the Minimum Description Length

principle. A distributed implementation based on iterative consensus techniques is then proposed, and it is shown effective

for managing outliers in the data.

JF - IEEE Workshop on Statistical Signal Processing (SSP)
CY - Palma de Mallorca, Spain
ER -
TY - CONF
T1 - Online EM-based distributed estimation in sensor networks with faulty nodes
T2 - European Signal Processing Conference (EUSIPCO)
Y1 - 2016
A1 - Pere Gimenez-Febrer
A1 - Alba Pagès-Zamora
A1 - R. López-Valcarce
KW - compass
KW - wsn
AB - This paper focuses on the problem of the distributed estimation of a parameter vector based on noisy observations regularly acquired by the nodes of a wireless sensor network and assuming that some of the nodes have faulty sensors. We propose two online schemes, both centralized and distributed, based on the Expectation-Maximization (EM) algorithm. These algorithms are able to identify and disregard the faulty nodes, and provide a refined estimate of the parameters each time instant after a new set of observations is acquired. Simulation results demonstrate that the centralized versions of the proposed online algorithms attain the same estimation error as the centralized batch EM, whereas the distributed versions come very close to matching the batch EM.

JF - European Signal Processing Conference (EUSIPCO)
CY - Budapest, Hungary
ER -
TY - CONF
T1 - Distributed AoA-based Source Positioning in NLOS with Sensor Networks
T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Y1 - 2015
A1 - Pere Gimenez-Febrer
A1 - Alba Pagès-Zamora
A1 - Silvana Silva Pereira
A1 - R. López-Valcarce
KW - compass
KW - wsn
AB - 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.

JF - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PB - IEEE
CY - Brisbane, Australia
ER -
TY - CONF
T1 - Distributed TLS Estimation under Random Data Faults
T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Y1 - 2015
A1 - Silvana Silva Pereira
A1 - Alba Pagès-Zamora
A1 - R. López-Valcarce
KW - compass
KW - wsn
AB - 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.

JF - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PB - IEEE
CY - Brisbane, Australia
ER -
TY - CONF
T1 - How to Implement Doubly-Stochastic Matrices for Consensus-Based Distributed Algorithms
T2 - IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
Y1 - 2014
A1 - S. Valcárcel-Macua
A1 - C. Moreno-León
A1 - J. S. Romero
A1 - Silvana Silva Pereira
A1 - Javier Zazo
A1 - Alba Pagès-Zamora
A1 - R. López-Valcarce
A1 - S. Zazo
KW - compass
KW - dynacs
KW - wsn
AB - 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.

JF - IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
CY - A Coruña, Spain
ER -