Hybrid three-dimensional variation and particle filtering for nonlinear systems
Hybrid three-dimensional variation and particle filtering for nonlinear systems
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DOI：
10.1088/1674-1056/22/3/030505
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发表时间：
2013-03
影响因子：
1.7
通讯作者：
Hong-Ze 洪泽 Leng 冷;Jun-Qiang 君强 Song 宋
中科院分区：
文献类型：
--
作者：
Hong-Ze 洪泽 Leng 冷;Jun-Qiang 君强 Song 宋
这项工作解决了利用稀疏观测估计非线性动态系统状态的问题。我们提出了一种混合三维变化（3DVar）和粒子抖动（PF）方法，它结合了 3DVar 和基于粒子的滤波器的优点。通过最小化成本函数，这种方法将产生更好的状态提案分布。之后，可以通过确定性方案避免标准 PF 中的随机重采样步骤。仿真结果表明，新方法的性能优于传统的集成卡尔曼滤波（EnKF）和标准PF，特别是在高度非线性系统中。
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.