Particle filter in multidimensional systems
Particle filter in multidimensional systems
复制标题
多维系统中的粒子过滤器
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
S. Drgas
中科院分区：
文献类型：
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作者：
Piotr Kozierski;Talar Sadalla;A. Owczarkowski;S. Drgas
本文介绍了应用于小型多维对象的粒子滤波器的估计质量的研究。为了本文的目的，提出了一种新型网络，其中每个节点都与一个状态变量相关联。根据执行的模拟，发现为小型系统（一维或二维）实现的粒子滤波器是一个不错的选择。然而，对于较大的物体，卡尔曼滤波器可能会返回更好的结果（这取决于所选的粒子数）。这是由于所需粒子数与物体尺寸呈指数依赖性。还观察到，与扩展卡尔曼滤波器相比，粒子滤波器更好地估计了计量良好的状态变量，同时更差地估计了计量较差的状态变量。还提出了针对具有大量状态变量的对象的可能方法，包括分散粒子滤波器。
The article presents studies on the estimation quality of a particle filter applied to small multidimensional objects. For the purposes of the article, a new type of network has been proposed, in which each node is associated with one state variable. Based on performed simulations it has been found that particle filter implemented for small systems (1- or 2-dimensional) is a good choice. However, for larger objects Kalman filter may return better results (it depends on the chosen particles number). This is due to the exponential dependence of needed particles number to the object dimension. It has been also observed that the particle filter, in comparison to the Extended Kalman filter, better estimates the state variables which are well metered, and simultaneously worse estimates the state variables which are worse metered. Possible approaches for objects with a greater number of state variables also have been adduced, including the dispersed particle filter.