Ensemble unscented Kalman filter for state inference in continuous–discrete systems

The Journal of Engineering

Bin Liu¹ 

[1] School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, People's Republic of China

关键词: discrete time instances;    continuous-discrete systems;    nonlinear state filtering problem;    CDUKF;    particle method;    noisy measurements;    state inference;    sequential importance sampling;    particle filter;    continuous-discrete unscented Kalman filter;    nonlinear systems;    stochastic differential equation;   

DOI  :  10.1049/joe.2014.0076

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

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.

【 授权许可】

CC BY