Hybrid three-dimensional variation and particle filtering for nonlinear systems

• 作 者: 冷洪泽 宋君强
• 机 构: College of Computer,National University of Defense Technology,
• 出 处: Chinese Physics B
• 发表日期: 2013年03期
• 关键词: filtering minimizing covariance deterministic proposal ensemble estimating chall

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.

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. Afterward 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.