论文标题
跳跃马尔可夫线性系统的新平滑算法
A New Smoothing Algorithm for Jump Markov Linear Systems
论文作者
论文摘要
本文提出了一种计算跳跃马尔可夫线性系统平滑状态分布的方法。更具体地说，本文详细介绍了一种新颖的两滤器更光滑的，该滤波器为平滑的混合状态分布提供了封闭形式的表达式。随着时间指数增加，该分布可以表示为高斯组件的高斯混合物。这伴随着记忆和计算需求的指数增长，这些增长迅速变得棘手。为了改善这一点，我们通过采用高斯混合物减少策略来限制允许的混合项的数量，从而导致计算上可拖动但近似平滑的分布。近似误差可以与计算复杂性平衡，以便提供一种准确，实用的平滑算法，该算法与现有的最新方法相比有利。
This paper presents a method for calculating the smoothed state distribution for Jump Markov Linear Systems. More specifically, the paper details a novel two-filter smoother that provides closed-form expressions for the smoothed hybrid state distribution. This distribution can be expressed as a Gaussian mixture with a known, but exponentially increasing, number of Gaussian components as the time index increases. This is accompanied by exponential growth in memory and computational requirements, which rapidly becomes intractable. To ameliorate this, we limit the number of allowed mixture terms by employing a Gaussian mixture reduction strategy, which results in a computationally tractable, but approximate smoothed distribution. The approximation error can be balanced against computational complexity in order to provide an accurate and practical smoothing algorithm that compares favourably to existing state-of-the-art approaches.
