A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
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DOI：
10.1109/78.978374
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发表时间：
2002-02-01
影响因子：
5.4
通讯作者：
Clapp, T
中科院分区：
文献类型：
--
作者：
Arulampalam, MS;Maskell, S;Clapp, T
对于许多应用领域，包含非线性和非高斯性的元素变得越来越重要，以便准确地模拟物理系统的底层动态：此外，从存储成本以及快速适应变化的信号特性的角度来看，在线处理数据通常至关重要。在本文中，我们回顾了非线性/非高斯跟踪问题的最优和次优贝叶斯算法，重点是粒子滤波器。粒子滤波器是基于概率密度的点质量（或“粒子”）表示的顺序蒙特卡罗方法，它可以应用于任何状态空间模型，并概括了传统的卡尔曼滤波方法：粒子滤波器的几个变种，如SIR，ASIR和RPF的顺序重要性采样（SIS）算法的通用框架内介绍。通过一个示例，对这些方法进行了讨论，并与标准EKF方法进行了比较.
Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system: Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods:. Several variants of the particle filter such as SIR, ASIR; and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.