Parameter estimation in general state-space models using particle methods
Parameter estimation in general state-space models using particle methods
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DOI：
10.1007/bf02530508
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发表时间：
2003-06-01
影响因子：
1
通讯作者：
Tadic, VB
中科院分区：
文献类型：
--
作者：
Doucet, A;Tadic, VB
粒子滤波技术是一组强大且通用的基于仿真的方法，用于在非线性非高斯状态空间模型中执行最佳状态估计。如果模型包含固定参数，则执行参数估计的标准技术包括用参数扩展状态以将问题转换为最优过滤问题。然而，这种方法需要使用特殊的粒子过滤技术，该技术存在一些缺点。我们在这里考虑一种结合粒子滤波和梯度算法的替代方法来执行批量和递归最大似然参数估计。提出了一种原始粒子方法来实现这些方法，并通过模拟评估其效率。
Particle filtering techniques are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. If the model includes fixed parameters, a standard technique to perform parameter estimation consists of extending the state with the parameter to transform the problem into an optimal filtering problem. However, this approach requires the use of special particle filtering techniques which suffer from several drawbacks. We consider here an alternative approach combining particle filtering and gradient algorithms to perform batch and recursive maximum likelihood parameter estimation. An original particle method is presented to implement these approaches and their efficiency is assessed through simulation.