An Implicit Algorithm of Solving Nonlinear Filtering Problems

被引：5
|
作者：
Bao, Feng [1 ]
Cao, Yanzhao [1 ,2 ]
Han, Xiaoying [1 ]
机构：
[1] Auburn Univ, Dept Math & Stat, Auburn, AL 36849 USA
[2] Sun Yat Sen Univ, Sch Math & Computat Sci, Guangzhou, Guangdong, Peoples R China
来源：
COMMUNICATIONS IN COMPUTATIONAL PHYSICS | 2014年 / 16卷 / 02期
基金：
美国国家科学基金会;
关键词：
Kalman filter; particle filter; implicit filter; Monte Carlo method; stochastic differential equations; KALMAN FILTER; ZAKAI EQUATION; BAYESIAN ESTIMATION; PARTICLE FILTERS; DISCRETIZATION; APPROXIMATION; CONVERGENCE; STATE;
D O I：
10.4208/cicp.180313.130214a
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
Nonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.
引用
收藏
页码：382 / 402
页数：21
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