| The Marginal Rao-Blackwellized Particle Filter for Mixed Linear/Nonlinear State Space Models |
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| 作者姓名： | Yin Jianjun Zhang Jianqiu Mike Klaas |
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| 作者单位： | [1]Department of Electronic Engineering, Fudan University, Shanghai 200433, China [2]Department of Computer Science, University of British Columbia, Vancouver V6T1Z4, Canada |
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| 基金项目： | National Natural Science Foundation of China （60572023） |
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| 摘 要： | In this paper, the marginal Rao-Blackwellized 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 MRBPF 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.
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| 关 键 词： | 信号处理 模拟 混合先性/非线性 地形半自动导航 粒子滤波器 |
| 修稿时间： | 2006-07-072007-03-20 |
The Marginal Rao-Blackwellized Particle Filter for Mixed Linear/Nonlinear State Space Models |
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| Yin Jianjun Zhang Jianqiu Mike Klaas.The Marginal Rao-Blackwellized Particle Filter for Mixed Linear/Nonlinear State Space Models[J].Chinese Journal of Aeronautics,2007,20(4):346-352. |
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| Authors: | Yin Jianjuna Zhang Jianqiua Mike Klaasb a |
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| Institution: | Yin Jianjuna,Zhang Jianqiua,Mike Klaasb aDepartment of Electronic Engineering,Fudan University,Shanghai 200433,China bDepartment of Computer Science,University of British Columbia,Vancouver V6T1Z4,Canada |
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| Abstract: | In this paper, the marginal Rao-Blackwellized 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 MRBPF 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. |
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| Keywords: | signal processing marginal Rao-Blackwellized particle filter simulation mixed linear/nonlinear terrain aided navigation |
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