Resum
This paper addresses the problem of distributed estimation of a parameter vector in the presence of noisy input and noisy 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 therefore 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.
Idioma original | Anglès |
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Títol de la publicació | 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Nombre de pàgines | 4 |
Volum | 2015 |
ISBN (electrònic) | 978-1-4673-6997-8 |
DOIs | |
Estat de la publicació | Publicada - 2015 |