@misc{10481/64263,
year = {2019},
month = {7},
url = {http://hdl.handle.net/10481/64263},
abstract = {The least-squares linear filtering problem is addressed for discrete-time stochastic signals, whose evolution
model is unknown and only the mean and covariance functions of the processes involved in the sensor measurement
equations are available instead. The sensor measured outputs are perturbed by additive noise and
different uncertainties, which are modelled in a unified way by random parameter matrices. Assuming that, at
each sampling time, the noises from the different sensors are cross-correlated with each other, the sequential
fusion architecture is adopted and the innovation technique is used to derive an easily implementable recursive
filtering algorithm. A simulation example is included to verify the effectiveness of the proposed sequential
fusion filter and analyze the influence of the sensor disturbances on the filter performance.},
organization = {Ministerio de Economía, Industria y Competitividad},
organization = {Agencia Estatal de Investigación},
organization = {Fondo Europeo de Desarrollo Regional FEDER MTM2017-84199-P},
publisher = {ScitePress},
keywords = {Sequential Fusion Filtering},
keywords = {Random Parameter Matrices},
keywords = {Cross-correlated Noises},
keywords = {Covariance-based Estimation},
keywords = {Sensor Networks},
title = {Optimal Filtering Algorithm based on Covariance Information using a Sequential Fusion Approach},
doi = {10.5220/0007786405870594},
author = {Caballero Águila, R. and Hermoso Carazo, Aurora and Linares Pérez, Josefa},
}