Open AccessEnsemble unscented Kalman filter for state inference in continuous–discrete systems
Author(s) -
Liu Bin
Publication year - 2014
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2014.0076
Subject(s) - kalman filter , computer science , ensemble kalman filter , particle filter , discrete time and continuous time , unscented transform , control theory (sociology) , extended kalman filter , stochastic differential equation , algorithm , inference , state (computer science) , mathematics , artificial intelligence , statistics , control (management)
The authors consider non‐linear state filtering problem in continuous–discrete systems, where the system dynamics is modelled by a stochastic differential equation, and noisy measurements of the system are obtained at discrete time instances. A novel particle method is proposed based on sequential importance sampling. This approach uses a bank of the continuous–discrete unscented Kalman filters (CDUKFs) to obtain the importance proposal distribution, retaining the advantage of the CDUKF in continuous–discrete systems as well as the accuracy of particle filter in highly non‐linear systems. Simulation results show that the algorithm outperforms some other benchmarks substantially in estimation accuracy.