  The distributed Kalman filter (DKF) has attracted extensive research as an
information fusion method for wireless sensor systems(WSNs). And the DKF in
non-Gaussian environments is still a pressing problem. In this paper, we
approximate the non-Gaussian noise as a Gaussian mixture model and estimate the
parameters through the expectation-maximization algorithm. A DKF, called model
fusion DKF (MFDKF) is proposed against the non-Gaussain noise. Specifically,
the proposed MFDKF is obtained by fusing the sub-models that are built based on
the noise approximation with the help of interacting multiple model (IMM).
Considering that some WSNs demand high consensus or have restricted
communication, consensus MFDKF (C-MFDKF) and simplified MFDKF (S-MFDKF) are
proposed based on consensus theory, respectively. The convergence of MFDKF and
its derivative algorithms are analyzed. A series of simulations indicate the
effectiveness of the MFDKF and its derivative algorithms.
