Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF

Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes, pp. 96–102

Abstract

State estimation in control and systems engineering traditionally requires extensive manual system identification or data-collection effort. However, transformer-based foundation models in other domains have reduced data requirements by leveraging pre-trained generalist models. Ultimately, developing zero-shot foundation models of system dynamics could drastically reduce manual deployment effort. While recent work shows that transformer-based end-to-end approaches can achieve zero-shot performance on unseen systems, they are limited to sensor models seen during training. We introduce the foundation model unscented Kalman filter (FM-UKF), which combines a transformer-based model of system dynamics with analytically known sensor models via an UKF, enabling generalization across varying dynamics without retraining for new sensor configurations. We evaluate FM-UKF on a new benchmark of container ship models with complex dynamics, demonstrating a competitive accuracy, effort, and robustness trade-off compared to classical methods with approximate system knowledge and to an end-to-end approach. The benchmark and dataset are open sourced to further support future research in zero-shot state estimation via foundation models.

Authors 5

1. Tobin Holtmann Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute for Data Science in Mechanical Engineering,Germany

2. David Stenger Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute for Data Science in Mechanical Engineering,Germany

3. Andrés Felipe Posada-Moreno Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute for Data Science in Mechanical Engineering,Germany

4. Friedrich Solowjow Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute for Data Science in Mechanical Engineering,Germany

5. Sebastian Trimpe Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute for Data Science in Mechanical Engineering,Germany