颗粒过滤器
计算机科学
非线性系统
扩展卡尔曼滤波器
卡尔曼滤波器
算法
高斯过程
贝叶斯概率
高斯分布
状态空间表示
重要性抽样
蒙特卡罗方法
光学(聚焦)
状态空间
辅助粒子过滤器
跟踪(教育)
集合卡尔曼滤波器
数学优化
人工智能
数学
统计
物理
心理学
教育学
量子力学
光学
作者
M. Sanjeev Arulampalam,Simon Maskell,Neil Gordon,Tim Clapp
摘要
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.
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