English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

A Model Fusion Distributed Kalman Filter For Non-Gaussian Observation Noise