Interacting multiple model particle filter
Interacting multiple model particle filter
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DOI：
10.1049/ip-rsn:20030741
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发表时间：
2003-10-01
期刊：
影响因子：
--
通讯作者：
Driessen, JN
中科院分区：
文献类型：
--
作者：
Boers, Y;Driessen, JN
针对马尔可夫切换系统，提出了一种新的多模型粒子滤波方法。这种新方法是交互多模型(IMM)滤波和(正则化)粒子滤波的组合。混合和相互作用类似于传统的IMM过滤器。然而，在每种模式下，都运行一个正则化粒子过滤器。正则化粒子滤波概率密度是高斯概率密度的混合。该方法能够处理非线性和非高斯噪声。此外，新方法在每个模式下保持固定的粒子数目，因此它不受现有马尔可夫切换系统的多模型粒子滤波器的潜在缺陷的影响。
A new method for multiple model particle filtering for Markovian switching systems is presented. This new method is a combination of the interacting multiple model (IMM) filter and a (regularised) particle filter. The mixing and interaction is similar to that in a conventional IMM filter. However, in every mode a regularised particle filter is running. The regularised particle filter probability density is a mixture of Gaussian probability densities. The proposed method is able to deal with nonlinearities and non-Gaussian noise. Furthermore, the new method keeps a fixed number of particles in each mode, and therefore it does not suffer from the potential drawbacks of existing multiple model particle filters for Markovian switching systems.