# EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise

arXiv: https://arxiv.org/abs/2509.07415 · HTML: https://mlsanity.com/paper/2509.07415/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [Outlier-Robust Filtering For Nonlinear Systems With Selective Observations Rejection](https://mlsanity.com/paper/2106.05706/) (2021)
- [Bayesian Heuristics for Robust Spatial Perception](https://mlsanity.com/paper/2212.00344/) (2022)
- [EMORF/S: EM-Based Outlier-Robust Filtering and Smoothing With Correlated Measurement Noise](https://mlsanity.com/paper/2307.02163/) (2023)

## Built on by (most cited first, 1 of 1)

- [Variational Outlier-Robust Gaussian Process Regression with Generative Modeling](https://mlsanity.com/paper/2608.16606/) (2026)

## Abstract (arXiv)

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.