@misc{10481/87149,
year = {2015},
month = {9},
url = {https://hdl.handle.net/10481/87149},
abstract = {This paper addresses the least-squares quadratic filtering problem in
discrete-time stochastic systems with random parameter matrices in both
the state and measurement equations. Defining a suitable augmented system, this problem is reduced to the least-squares linear filtering problem
of the augmented state based on the augmented observations. Under the
assumption that the moments, up to the fourth-order one, of the original
state and measurement vectors are known, a recursive algorithm for the optimal linear filter of the augmented state is designed, from which the optimal
quadratic filter of the original state is obtained. As a particular case, the
proposed results are applied to multi-sensor systems with state-dependent
multiplicative noise and fading measurements and, finally, a numerical simulation example illustrates the performance of the proposed quadratic filter
in comparison with the linear one and also with other filters in the existing
literature.},
organization = {Ministerio de Economía y Competitividad (Grant No. MTM2014-52291-P and FPU programme)},
publisher = {Elsevier},
keywords = {Random parameter matrices},
keywords = {Least-squares estimation},
keywords = {Fading measurements},
keywords = {Recursive filtering algorithm},
title = {Quadratic estimation problem in discrete-time stochastic systems with random parameter matrices},
doi = {10.1016/j.amc.2015.10.005},
author = {Caballero-Águila, R. and García Garrido, Irene and Linares Pérez, Josefa},
}