A Projection-Based Rao-Blackwellized Particle Filter to Estimate Parameters in Conditionally Conjugate State-Space Models

Milan Papez
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引用次数: 1

Abstract

Particle filters constitute today a well-established class of techniques for state filtering in non-linear state-space models. However, online estimation of static parameters under the same framework represents a difficult problem. The solution can be found to some extent within a category of state-space models allowing us to perform parameter estimation in an analytically tractable manner, while still considering non-linearities in data evolution equations. Nevertheless, the well-known particle path degeneracy problem complicates the computation of the statistics that are required to estimate the parameters. The present paper proposes a simple and efficient method which is experimentally shown to suffer less from this issue.
基于投影的rao - blackwelzed粒子滤波在条件共轭状态空间模型中的参数估计
粒子滤波是目前在非线性状态空间模型中建立的一类行之有效的状态滤波技术。然而,在相同的框架下,静态参数的在线估计是一个难题。在某种程度上,可以在一类状态空间模型中找到解决方案,这些模型允许我们以一种解析易于处理的方式执行参数估计,同时仍然考虑数据演化方程中的非线性。然而,众所周知的粒子路径退化问题使估计参数所需的统计量的计算复杂化。本文提出了一种简单有效的方法,实验证明该方法受此问题的影响较小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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