# An Efficient Method for the Joint Estimation of System Parameters and Noise Covariances for Linear Time-Variant Systems

arXiv: https://arxiv.org/abs/2211.12302 · HTML: https://mlsanity.com/paper/2211.12302/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification](https://mlsanity.com/paper/2502.02133/) (2025)
- [The Mini Wheelbot: A Testbed for Learning-based Balancing, Flips, and Articulated Driving](https://mlsanity.com/paper/2502.04582/) (2025)
- [Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements](https://mlsanity.com/paper/2610.16559/) (2026)

## Abstract (arXiv)

We present an optimization-based method for the joint estimation of system parameters and noise covariances of linear time-variant systems. Given measured data, this method maximizes the likelihood of the parameters. We solve the optimization problem of interest via a novel structure-exploiting solver. We present the advantages of the proposed approach over commonly used methods in the framework of Moving Horizon Estimation. Finally, we show the performance of the method through numerical simulations on a realistic example of a thermal system. In this example, the method can successfully estimate the model parameters in a short computational time.