In this manuscript, a general method for deriving filtering algorithms that involve a network of interconnected Bayesian filters is proposed. This method is based on the idea that the processing accomplished inside each of the Bayesian filters and the interactions between them can be represented as message passing algorithms over a proper graphical model. The usefulness of our method is exemplified by developing new filtering techniques, based on the interconnection of a particle filter and an extended Kalman filter, for conditionally linear Gaussian systems. Numerical results for two specific dynamic systems evidence that the devised algorithms can achieve a better complexity-accuracy tradeoff than marginalized particle filtering and multiple particle filtering.

中文翻译：

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作为消息传递的多重贝叶斯过滤


在这份手稿中，提出了一种导出涉及互连贝叶斯滤波器网络的过滤算法的通用方法。该方法基于这样的思想：每个贝叶斯滤波器内部完成的处理以及它们之间的交互可以表示为通过适当的图形模型的消息传递算法。我们的方法的实用性通过为条件线性高斯系统开发基于粒子滤波器和扩展卡尔曼滤波器互连的新滤波技术来例证。两个特定动态系统的数值结果证明，所设计的算法可以实现比边缘化粒子滤波和多粒子滤波更好的复杂性与精度权衡。