论文标题
粒子过滤在一般制度转换下
Particle Filtering Under General Regime Switching
论文作者
论文摘要
在本文中，我们考虑了模型不确定性下粒子过滤的新框架，该框架超出了马尔可夫开关系统的范围。具体而言，我们开发了一种新型的粒子过滤算法，该算法适用于通用状态切换系统，其中模型索引被增强为系统中未知的时变参数。所提出的方法不需要使用多个过滤器，并且可以通过适当选择粒子过滤提案分布来维护每个考虑模型的各种粒子。提出的方法的灵活性允许模型之间长期依赖性，从而使其用于更广泛的现实应用程序。我们在合成数据实验上验证了该方法，并表明它的表现优于需要使用多个滤镜的最先进的多个模型粒子过滤方法。
In this paper, we consider a new framework for particle filtering under model uncertainty that operates beyond the scope of Markovian switching systems. Specifically, we develop a novel particle filtering algorithm that applies to general regime switching systems, where the model index is augmented as an unknown time-varying parameter in the system. The proposed approach does not require the use of multiple filters and can maintain a diverse set of particles for each considered model through appropriate choice of the particle filtering proposal distribution. The flexibility of the proposed approach allows for long-term dependencies between the models, which enables its use to a wider variety of real-world applications. We validate the method on a synthetic data experiment and show that it outperforms state-of-the-art multiple model particle filtering approaches that require the use of multiple filters.
