Numerical fitting‐based likelihood calculation to speed up the particle filter
Numerical fitting‐based likelihood calculation to speed up the particle filter
复制标题
DOI：
10.1002/acs.2656
复制
发表时间：
2013-08
影响因子：
3.1
通讯作者：
Tiancheng Li;Shudong Sun;J. Corchado;T. Sattar;Shubin Si
中科院分区：
文献类型：
--
作者：
Tiancheng Li;Shudong Sun;J. Corchado;T. Sattar;Shubin Si
在观测模型复杂的应用中，特别是涉及地图或图像处理的应用中，大量粒子的似然计算成为粒子滤波的计算瓶颈。在本文中，提出了一种数值拟合方法来加速粒子滤波器，其中粒子的似然是基于少量所谓的支点的似然来解析地推断/拟合的。结果表明，采用适当的拟合函数和适当的支点分布，可以获得较好的估计精度。分别对拟合函数和支点的构造进行了详细讨论。为了避免多维模型中难以处理的多元拟合，隐式似然拟合可以采用非参数核密度估计器，如最近邻光滑器或均匀核平均光滑器。给出了基于基准一维模型和多维移动机器人定位的仿真结果。版权所有©2015 John Wiley & Sons， Ltd
The likelihood calculation of a vast number of particles forms the computational bottleneck for the particle filter in applications where the observation model is complicated, especially when map or image processing is involved. In this paper, a numerical fitting approach is proposed to speed up the particle filter in which the likelihood of particles is analytically inferred/fitted, explicitly or implicitly, based on that of a small number of so‐called fulcrums. It is demonstrated to be of fairly good estimation accuracy when an appropriate fitting function and properly distributed fulcrums are used. The construction of the fitting function and fulcrums are addressed respectively in detail. To avoid intractable multivariate fitting in multi‐dimensional models, a nonparametric kernel density estimator such as the nearest neighbor smoother or the uniform kernel average smoother can be employed for implicit likelihood fitting. Simulations based on a benchmark one‐dimensional model and multi‐dimensional mobile robot localization are provided. Copyright © 2015 John Wiley & Sons, Ltd.