Mixture Kalman filters
Mixture Kalman filters
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DOI：
10.1111/1467-9868.00246
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发表时间：
2000-01-01
影响因子：
5.8
通讯作者：
Liu, JS
中科院分区：
文献类型：
--
作者：
Chen, R;Liu, JS
在处理动态系统时，序贯Monte Carte方法使用离散样本来表示复杂的概率分布，并使用拒绝抽样、重要抽样和加权响应来完成在线“滤波”任务。我们提出了一种特殊的顺序蒙特卡罗方法，混合卡尔曼滤波器，它使用一个随机混合的高斯分布近似的目标分布。它被设计用于在线估计和预测的条件和部分条件动态线性模型，这本身是一类广泛使用的非线性系统，也可以近似许多其他。与包括Monte Carte方法在内的几种可用的滤波方法相比，由混合卡尔曼滤波器提供的效率增益可以是非常可观的。本文的另一个贡献是将许多非线性系统转化为条件或部分条件线性形式，可以应用混合卡尔曼滤波器。给出了目标跟踪和数字通信中的例子来演示所提出的方法。
In treating dynamic systems, sequential Monte Carte methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the on-line 'filtering' task. We propose a special sequential Monte Carlo method, the mixture Kalman filter, which uses a random mixture of the Gaussian distributions to approximate a target distribution. It is designed for on-line estimation and prediction of conditional and partial conditional dynamic linear models, which are themselves a class of widely used non-linear systems and also serve to approximate many others. Compared with a few available filtering methods including Monte Carte methods, the gain in efficiency that is provided by the mixture Kalman filter can be very substantial. Another contribution of the paper is the formulation of many non-linear systems into conditional or partial conditional linear form, to which the mixture Kalman filter can be applied. Examples in target tracking and digital communications are given to demonstrate the procedures proposed.