Convergence Properties of Particle Filter Algorithm

Yanwen Qu, Yi Chen, Jing-yu Yang
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引用次数: 1

Abstract

The basic sampling importance resampling algorithm is the basic for improving particle filter methods which are widely utilized in optimal filtering problems. In our paper, we introduce a modified basic SIR algorithm and analyze the convergence property of the modified basic SIR algorithm. Furthermore, when the recursive time is finite and the forth-order moment of the interesting function w.r.t the posterior joint distribution of the extended state is exist, the sufficient condition for the basic particle filter estimation convergence almost surely to the optimal estimation is discussed.
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粒子滤波算法的收敛性
基本采样重要性重采样算法是改进粒子滤波方法的基础,粒子滤波方法广泛应用于最优滤波问题。本文介绍了一种改进的基本SIR算法,并分析了改进的基本SIR算法的收敛性。进一步讨论了当递推时间有限且感兴趣函数的四阶矩w.r.t扩展状态的后验联合分布存在时,基本粒子滤波估计几乎肯定收敛到最优估计的充分条件。
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