This paper is concerned with the fusion filtering and fixed-point smoothing problems for a class of networked
systems with multiple random uncertainties in both the sensor outputs and the transmission connections. To deal
with this kind of systems, random parameter matrices are considered in the mathematical models of both the
sensor measurements and the data available after transmission. The additive noise in the transmission channel
from each sensor is assumed to be sequentially time-correlated. By using the time-differencing approach, the
available measurements are transformed into an equivalent set of observations that do not depend on the timecorrelated
noise. The innovation approach is then applied to obtain recursive distributed and centralized fusion
estimation algorithms for the filtering and fixed-point smoothing estimators of the signal based on the transformed
measurements, which are equal to the estimators based on the original ones. The derivation of the algorithms
does not require the knowledge of the signal evolution model, but only the mean and covariance functions of
the processes involved (covariance information). A simulation example illustrates the utility and effectiveness of
the proposed fusion estimation algorithms, as well as the applicability of the current model to deal with different
network-induced random phenomena.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
