@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} }