论文标题
顺序贝叶斯推断不确定的非线性动态系统：教程
Sequential Bayesian Inference for Uncertain Nonlinear Dynamic Systems: A Tutorial
论文作者
论文摘要
在本文中，提出了贝叶斯方法的贝叶斯方法概述，用于从非线性和非高斯动态系统的后验分布中进行顺序仿真。该重点主要放在顺序的蒙特卡洛方法上，这些方法基于概率密度的粒子表示，并且可以无缝地将其概括为任何状态空间表示。在这种情况下，根据稀疏测量值，提出了各种粒子过滤器（PF）替代方案的统一框架，以解决状态，状态参数和输入状态参数估计问题。每个过滤器的算法步骤均得到彻底显示，并将一个简单的说明性示例用于推断i）未观察到的状态，ii）未知系统参数和iii）未衡量的驱动输入。
In this article, an overview of Bayesian methods for sequential simulation from posterior distributions of nonlinear and non-Gaussian dynamic systems is presented. The focus is mainly laid on sequential Monte Carlo methods, which are based on particle representations of probability densities and can be seamlessly generalized to any state-space representation. Within this context, a unified framework of the various Particle Filter (PF) alternatives is presented for the solution of state, state-parameter and input-state-parameter estimation problems on the basis of sparse measurements. The algorithmic steps of each filter are thoroughly presented and a simple illustrative example is utilized for the inference of i) unobserved states, ii) unknown system parameters and iii) unmeasured driving inputs.
