Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks
Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks
复制标题
动态贝叶斯网络中粒子滤波的并行子空间采样
DOI：
10.1007/978-3-642-04180-8_26
复制
发表时间：
2009
影响因子：
1.6
通讯作者：
T. Lane
中科院分区：
文献类型：
--
作者：
E. Besada;S. Plis;J. M. Cruz;T. Lane
由于其固有的复杂性和不确定性，监测现实世界动态系统的变量是一项艰巨的任务。粒子过滤器（PF）执行这项任务，产生未观察到的变量的概率分布。然而，它们受到维数问题的困扰：粒子的数量随着隐藏状态空间的维数呈指数增长。当变量的初始分布不为人所知时，就像在全局局部化问题中发生的那样，问题会更加严重。本文提出了一种新的动态贝叶斯网络系统的并行预测算法。新算法显著减少了粒子数量，同时独立探索隐藏变量的不同子空间，以构建与过去历史和测量一致的粒子。我们在一些复杂的动态系统估计问题上展示了这种新的PF方法，表明我们的方法在传统PF失败的情况下成功地定位和跟踪隐藏状态。
Monitoring the variables of real world dynamic systems is a difficult task due to their inherent complexity and uncertainty. Particle Filters (PF) perform that task, yielding probability distribution over the unobserved variables. However, they suffer from the curse of dimensionality problem: the number of particles grows exponentially with the dimensionality of the hidden state space. The problem is aggravated when the initial distribution of the variables is not well known, as happens in global localization problems. We present a new parallel PF for systems whose variable dependencies can be factored into a Dynamic Bayesian Network. The new algorithms significantly reduce the number of particles, while independently exploring different subspaces of hidden variables to build particles consistent with past history and measurements. We demonstrate this new PF approach on some complex dynamical system estimation problems, showing that our method successfully localizes and tracks hidden states in cases where traditional PFs fail.