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.
|Original language||English (US)|
|Title of host publication||2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016|
|Publisher||IEEE Computer Society|
|State||Published - Aug 24 2016|
|Event||19th IEEE Statistical Signal Processing Workshop, SSP 2016 - Palma de Mallorca, Spain|
Duration: Jun 25 2016 → Jun 29 2016
|Name||IEEE Workshop on Statistical Signal Processing Proceedings|
|Other||19th IEEE Statistical Signal Processing Workshop, SSP 2016|
|City||Palma de Mallorca|
|Period||6/25/16 → 6/29/16|
Bibliographical notePublisher Copyright:
© 2016 IEEE.
- Multivariate regression
- distributed estimation
- wireless sensor networks