Quadratic Filtering Algorithm Based on Covariances Using Correlated Uncerta in Observations Coming from Different Sensors Caballero Águila, Raquel Hermoso Carazo, Aurora Linares Pérez, Josefa The least-squares quadratic estimation problem of signals from observations coming from multiple sensors is addressed when there is a nonzero probability that each observation does not contain the signal to be estimated. We assume that, at each sensor, the uncertainty about the signal being present or missing in the observation is modelled by correlated Bernoulli random variables, whose probabilities are not necessarily the same for all the sensors. A recursive algorithm is derived without requiring the knowledge of the signal state-space model but only the moments (up to the fourth-order ones) of the signal and observation noise, the uncertainty probabilities, and the correlation between the variables modelling the uncertainty. The estimators require the autocovariance and cross-covariance functions of the signal and their second-order powers in a semidegenerate kernel form. The recursive quadratic filtering algorithm is derived from a linear estimation algorithm for a suitably defined augmented system. 2024-12-13T09:37:38Z 2024-12-13T09:37:38Z 2011-06-30 journal article Caballero-Águila, R., Hermoso-Carazo, A., Linares-Pérez, J., Quadratic Filtering Algorithm Based on Covariances Using Correlated Uncertain Observations Coming from Different Sensors, International Scholarly Research Notices, 2011, 148461, 18 pages, 2011. https://doi.org/10.5402/2011/148461 https://hdl.handle.net/10481/97979 10.5402/2011/148461 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Wiley