Hybrid three-dimensional variation and particle filtering for nonlinear systems

doi:10.1088/1674-1056/22/3/030505

Abstract This work addresses the problem of estimating the states of nonlinear dynamic systems with sparse observations. We present a hybrid three-dimensional variation (3DVar) and particle piltering (PF) method, which combines the advantages of 3DVar and particle-based filters. By minimizing the cost function, this approach will produce a better proposal distribution of the state. Afterwards the stochastic resampling step in standard PF can be avoided through a deterministic scheme. The simulation results show that the performance of the new method is superior to the traditional ensemble Kalman filtering (EnKF) and the standard PF, especially in highly nonlinear systems.

Keywords: three-dimensional variation (3DVar) particle piltering (PF) ensemble Kalman filtering (EnKF) chaos system

Received: 10 June 2012
Revised: 07 September 2012
Accepted manuscript online:

PACS:	05.45.-a	(Nonlinear dynamics and chaos)
	02.60.-x	(Numerical approximation and analysis)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 41105063).

Corresponding Authors: Leng Hong-Ze
E-mail: hzleng@nudt.edu.cn

Cite this article:

Leng Hong-Ze (冷洪泽), Song Jun-Qiang (宋君强) Hybrid three-dimensional variation and particle filtering for nonlinear systems 2013 Chin. Phys. B 22 030505

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