Consensus-Based Distributed Multiple Model UKF for Jump Markov Nonlinear Systems

• Published in: IEEE transactions on automatic control Vol. 57; no. 1; pp. 227 - 233
• Main Authors: Li, Wenling, Jia, Yingmin
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Computer science; control theory; systems; Computer simulation; Computer systems and distributed systems. User interface; Consensus theory; Control theory. Systems; Covariance matrix; distributed estimation; Dynamical systems; Estimation; Exact sciences and technology; Filtering; Filtration; Heuristic algorithms; jump Markov nonlinear system (JMNLS); Kalman filters; Markov processes; Modelling and identification; Niobium; Nonlinear dynamics; Nonlinear systems; Software; Tracking; unscented Kalman filter (UKF); Multimodel; Multimodel control; distributed estimation; Target tracking; Filtering; Optimal filter; Non linear control; Distributed system; jump Markov nonlinear system (JMNLS); Non linear system; Consensus theory; Consensus; Hybrid system; Discrete time; System identification; Sensor array; Jumping; Unscented Kalman filter; unscented Kalman filter (UKF)
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2011.2161838

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.