Distributed consensus filtering for discrete-time nonlinear systems with non-Gaussian noise

Authors:
Wenling Li;Yingmin Jia
Affiliations:
The Seventh Research Division, Beihang University (BUAA), Beijing 100191, China and The Department of Systems and Control, Beihang University (BUAA), Beijing 100191, China;The Seventh Research Division, Beihang University (BUAA), Beijing 100191, China and The Department of Systems and Control, Beihang University (BUAA), Beijing 100191, China and Key Laboratory of Ma ...
Venue:
Signal Processing
Year:
2012

Citing 15
Cited 0

Average consensus problems in networks of agents with delayed communications

Automatica (Journal of IFAC)
Polynomial filtering for fast convergence in distributed consensus

IEEE Transactions on Signal Processing
Accelerated distributed average consensus via localized node state prediction

IEEE Transactions on Signal Processing
Distributed variational Bayesian algorithms for Gaussian mixtures in sensor networks

Signal Processing
Brief paper: Discrete-time dynamic average consensus

Automatica (Journal of IFAC)
Distributed consensus filtering in sensor networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast distributed average consensus algorithms based on advection-diffusion processes

IEEE Transactions on Signal Processing
Adaptive fast consensus algorithm for distributed sensor fusion

Signal Processing
Likelihood function modeling of particle filter in presence of non-stationary non-gaussian measurement noise

Signal Processing
Brief paper: Distributed H∞-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case

Automatica (Journal of IFAC)
Gaussian particle filtering

IEEE Transactions on Signal Processing
Gaussian sum particle filtering

IEEE Transactions on Signal Processing
Decentralized sigma-point information filters for target tracking in collaborative sensor networks

IEEE Transactions on Signal Processing - Part II
Distributing the Kalman Filter for Large-Scale Systems

IEEE Transactions on Signal Processing - Part I
Distributed Kalman filtering based on consensus strategies

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.08

Visualization

Abstract

This paper studies the problem of distributed estimation for a class of discrete-time nonlinear non-Gaussian systems in a not fully connected sensor network environment. The non-Gaussian process noise and measurement noise are approximated by finite Gaussian mixture models. A distributed Gaussian mixture unscented Kalman filter (UKF) is developed in which each sensor node independently calculates local statistics by using its own measurement and an average-consensus filter is utilized to diffuse local statistics to its neighbors. A main difficulty encountered is the distributed computation of the Gaussian mixture weights, which is overcome by introducing the natural logarithm transformation. The effectiveness of the proposed distributed filter is verified via a simulation example involving tracking a target in the presence of glint noise.