改进的鲁棒滤波器，能自适应处理相关测量噪声中的异常值。
EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise
- 基于期望最大化框架，边推断边学习异常值特征
- 相比现有方法精度提升，计算开销略有增加但可控
- 适合需要高鲁棒性的实时系统应用
我们提出一种面向一般场景下存在相关测量噪声的基于学习的异常值鲁棒滤波器。作为基于期望最大化（EM）的异常值鲁棒滤波器（EMORF）的增强版本，命名为EMORF-II。该方法在推理过程中具备学习异常值特征的能力，并与异常值检测相结合，显著提升了异常值抑制能力。数值实验表明，相较于当前最优方法，其在准确性上取得提升，计算开销略有增加。然而，其计算复杂度阶数仍与其它实用方法相当，因此适用于多种应用场景。
原文摘要 · Abstract (English)
We present a learning-based outlier-robust filter for a general setup where the measurement noise can be correlated. Since it is an enhanced version of EM-based outlier robust filter (EMORF), we call it as EMORF-II. As it is equipped with an additional powerful feature to learn the outlier characteristics during inference along with outlier-detection, EMORF-II has improved outlier-mitigation capability. Numerical experiments confirm performance gains as compared to the state-of-the-art methods in terms of accuracy with an increased computational overhead. However, thankfully the computational complexity order remains at par with other practical methods making it a useful choice for diverse applications.
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