# Robust online joint state/input/parameter estimation of linear systems

arXiv: https://arxiv.org/abs/2204.05663 · HTML: https://mlsanity.com/paper/2204.05663/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Regularization via Mass Transportation](https://mlsanity.com/paper/1710.10016/) (2017)
- [Proximal Algorithms in Statistics and Machine Learning](https://mlsanity.com/paper/1502.03175/) (2015)
- [Online Joint State Inference and Learning of Partially Unknown State-Space Models](https://mlsanity.com/paper/2102.07418/) (2021)
- [An adaptive Ridge procedure for L0 regularization](https://mlsanity.com/paper/1505.01949/) (2015)
- [Robustified Multivariate Regression and Classification Using Distributionally Robust Optimization under the Wasserstein Metric](https://mlsanity.com/paper/2006.06090/) (2020)
- [Learning the tuned liquid damper dynamics by means of a robust EKF](https://mlsanity.com/paper/2103.03520/) (2021)
- [Robust Subspace System Identification via Weighted Nuclear Norm Optimization](https://mlsanity.com/paper/1312.2132/) (2013)

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

- [Maximum likelihood inference for high-dimensional problems with multiaffine variable relations](https://mlsanity.com/paper/2409.03495/) (2024)

## Abstract (arXiv)

This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion. The method is specially designed for measurements that are corrupted with non-Gaussian noise or outliers, which are commonly found in engineering applications. In particular, it combines recursive, alternating, and iteratively-reweighted least squares into a single, one-step algorithm, which solves the estimation problem online and benefits from the robustness of least-deviation regression methods. The convergence of the iterative method is formally guaranteed. Numerical experiments show the good performance of the estimation algorithm in presence of outliers and in comparison to state-of-the-art methods.