Quadratic estimation problem in discrete-time stochastic systems with random parameter matrices Caballero-Águila, R. García Garrido, Irene Linares Pérez, Josefa Random parameter matrices Least-squares estimation Fading measurements Recursive filtering algorithm 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. 2024-01-23T10:41:35Z 2024-01-23T10:41:35Z 2015-09-30 journal article Published version: Caballero-Águila, R., García-Garrido, I., Linares-Pérez, J., (2016), Quadratic estimation problem in discrete-time stochastic systems with random parameter matrices, Applied Mathematics and Computation, Vol 273, 308-320. https://doi.org/10.1016/j.amc.2015.10.005 https://hdl.handle.net/10481/87149 10.1016/j.amc.2015.10.005 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier