@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 = {<div>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,</div><div>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.</div>},
	keywords = {compass, wsn},
	author = {Silvana Silva Pereira and Alba Pag{\`e}s-Zamora and R. L{\'o}pez-Valcarce}
}
