Particle filters for state-space models with the presence of unknown static parameters
Particle filters for state-space models with the presence of unknown static parameters
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DOI：
10.1109/78.978383
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发表时间：
2002-02
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影响因子：
--
通讯作者：
G. Storvik
中科院分区：
文献类型：
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作者：
G. Storvik
粒子滤波器的动态状态空间模型处理未知的静态参数进行了讨论。该方法是基于边缘化的静态参数的后验分布，使只有状态向量需要考虑。这种边缘化总是可以适用的。然而，实时应用是唯一可能的，当给定的观测值和隐藏的状态向量的未知参数的分布取决于一些低维充分的统计。在许多常用的状态空间模型中，都有这样的充分统计量。将静态参数边缘化避免了当静态参数作为状态向量的一部分被包括时通常发生的重叠问题。过滤器进行了测试，在几个不同的模型，有希望的结果。
Particle filters for dynamic state-space models handling unknown static parameters are discussed. The approach is based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered. Such a marginalization can always be applied. However, real-time applications are only possible when the distribution of the unknown parameters given both observations and the hidden state vector depends on some low-dimensional sufficient statistics. Such sufficient statistics are present in many of the commonly used state-space models. Marginalizing the static parameters avoids the problem of impoverishment, which typically occurs when static parameters are included as part of the state vector. The filters are tested on several different models, with promising results.