Title :
Consensus-Based Distributed Multiple Model UKF for Jump Markov Nonlinear Systems
Author :
Wenling Li ; Yingmin Jia
Author_Institution :
Dept. of Syst. & Control, Beihang Univ. (BUAA), Beijing, China
Abstract :
This note studies the problem of distributed estimation for jump Markov nonlinear systems (JMNLSs) in a not fully connected sensor network. Based on the consensus theory, a distributed unscented Kalman filter (UKF) is first derived for nonlinear systems without jumping parameters and then it is extended to develop a distributed multiple model UKF for JMNLSs. The proposed filtering algorithm is illustrated via a simulation example involving tracking a maneuvering target.
Keywords :
Kalman filters; Markov processes; filtering theory; nonlinear systems; target tracking; JMNLS; UKF; connected sensor network; consensus theory; consensus-based distributed multiple model UKF; distributed unscented Kalman filter; filtering algorithm; jump Markov nonlinear system; Covariance matrix; Estimation; Heuristic algorithms; Kalman filters; Markov processes; Niobium; Nonlinear systems; Consensus theory; distributed estimation; jump Markov nonlinear system (JMNLS); unscented Kalman filter (UKF);
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2011.2161838
