论文标题
具有稳健粒子滤波器的创新性和添加性异常稳健的卡尔曼过滤
Innovative And Additive Outlier Robust Kalman Filtering With A Robust Particle Filter
论文作者
论文摘要
在本文中，我们提出了CE-Bass，CE-Bass是一种粒子混合物Kalman滤波器，对创新和添加剂异常值都有鲁棒性，并且能够完全捕获隐藏状态分布中的多模式。此外，粒子采样方法将其重新示例过时，这使CE-Bass能够处理创新的异常值，而这些异常值在观测值中不立即可见，例如趋势变化。当我们得出对粒子的最佳建议分布的新，准确的近似值时，该过滤器在计算上是有效的。所提出的算法显示可以与现有方法进行良好的比较，并应用于机器温度和服务器数据。
In this paper, we propose CE-BASS, a particle mixture Kalman filter which is robust to both innovative and additive outliers, and able to fully capture multi-modality in the distribution of the hidden state. Furthermore, the particle sampling approach re-samples past states, which enables CE-BASS to handle innovative outliers which are not immediately visible in the observations, such as trend changes. The filter is computationally efficient as we derive new, accurate approximations to the optimal proposal distributions for the particles. The proposed algorithm is shown to compare well with existing approaches and is applied to both machine temperature and server data.
