Improved particle filter for nonlinear problems
Improved particle filter for nonlinear problems
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DOI：
10.1049/ip-rsn:19990255
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发表时间：
1999-02-01
期刊：
影响因子：
--
通讯作者：
Fearnhead, P
中科院分区：
文献类型：
--
作者：
Carpenter, J;Clifford, P;Fearnhead, P
卡尔曼滤波器为线性高斯滤波问题提供了一种有效的解决方案。然而，在模型规格或观测过程中存在非线性的情况下，需要使用其他方法。一般被称为“粒子滤波器”的方法被认为是。这些包括凝聚算法和贝叶斯引导或抽样重要性响应（SIR）过滤器。这些过滤器表示状态变量的后验分布的粒子系统的演变和适应递归新的信息变得可用。在实践中，可能需要大量的粒子来提供足够的近似，并且对于某些应用，在一系列更新之后，粒子系统通常会塌陷到单个点。监测这些过滤器的效率的方法介绍了提供了一个简单的定量评估的样品dashion和作者展示了如何构建改进的粒子过滤器，既结构上有效的防止崩溃的粒子系统和计算效率在其实施。经典的纯方位跟踪问题就说明了这一点。
The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However where there is nonlinearity, either in the model specification or the observation process, other methods are required. Methods known generically as 'particle filters' are considered. These include the condensation algorithm and the Bayesian bootstrap or sampling importance resampling (SIR) filter. These filters represent the posterior distribution of the state variables by a system of particles which evolves and adapts recursively as new information becomes available. In practice, large numbers of particles may be required to provide adequate approximations and for certain applications, after a sequence of updates, the particle system will often collapse to a single point. A method of monitoring the efficiency of these filters is introduced which provides a simple quantitative assessment of sample impoverishment and the authors show how to construct improved particle filters that are both structurally efficient in terms of preventing the collapse of the particle system and computationally efficient in their implementation. This is illustrated with the classic bearings-only tracking problem.