EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise
This addresses outlier mitigation in filtering applications for domains like signal processing or robotics, though it appears incremental as an enhanced version of an existing method.
The paper tackles the problem of outlier-robust filtering with correlated measurement noise by introducing EMORF-II, an enhanced version of EMORF that learns outlier characteristics during inference, resulting in improved accuracy compared to state-of-the-art methods.
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.