The marginal Rao-Blackwellized particle filter for mixed linear/nonlinear state space models

被引：24

作者：

Yin Jianjun ^{[1
]}

Zhang Jianqiu ^{[1
]}

Klaas, Mike ^{[2
]}

机构：

[1] Fudan Univ, Dept Elect Engn, Shanghai 200433, Peoples R China

[2] Univ British Columbia, Dept Comp Sci, Vancouver, BC V6T 1Z4, Canada

来源：

CHINESE JOURNAL OF AERONAUTICS | 2007年 / 20卷 / 04期

基金：

中国国家自然科学基金;

关键词：

signal processing; marginal Rao-Blackwellized particle filter; simulation; mixed linear/nonlinear; terrain aided navigation;

D O I：

10.1016/S1000-9361(07)60054-5

中图分类号：

V [航空、航天];

学科分类号：

08 [工学]; 0825 [航空宇航科学与技术];

摘要：

In this paper, the marginal Rao-Black-wellized particle filter (MRBPF), which fuses the Rao-Blackwellized particle filter (RBPF) algorithm and the marginal particle filter (MPF) algorithm, is presented. The state space is divided into linear and non-linear parts, which can be estimated separately by the MPF and the optional Kalman filter. Through simulation in the terrain aided navigation (TAN) domain, it is demonstrated that, compared with the RBPF, the root mean square errors (RMSE) and the error variance of the nonlinear state estimations by the proposed NMPF are respectively reduced by 29% and 96%, while the unique particle count is increased by 80%. It is also found that the MRBPF has better convergence properties, and analysis has shown that the existing RBPF is nothing more than a special case of the MRBPF.

引用

页码：346 / 352

页数：7

共 11 条

[1]

Bergman N., 1999, THESIS LINKOPING U

[2]

Doucet A., 2001, SEQUENTIAL MONTE CAR, V1, DOI [10.1007/978-1-4757-3437-9, DOI 10.1007/978-1-4757-3437-9]

[3]

NOVEL-APPROACH TO NONLINEAR NON-GAUSSIAN BAYESIAN STATE ESTIMATION [J].