采用鲁棒粒子滤波器的创新和可加离群鲁棒卡尔曼滤波
Innovative And Additive Outlier Robust Kalman Filtering With A Robust Particle Filter
摘要：
在本文中，我们提出了CE-BASS，一个粒子混合卡尔曼滤波器，这是强大的创新和添加剂离群值，并能够充分捕捉多模态的隐藏状态的分布。此外，粒子采样方法对过去的状态进行重新采样，这使得CE-BASS能够处理在观测中无法立即看到的创新离群值，例如趋势变化。过滤器是计算效率高，因为我们得到新的，准确的近似粒子的最佳建议分布。所提出的算法与现有的方法进行比较，并适用于机器温度和服务器数据。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
