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

• URL: http://arxiv.org/abs/2509.04213v1
• Date: Thu, 04 Sep 2025 13:38:54 GMT
• Title: Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF
• Abstract summary: We introduce the foundation model Kalman filter (FM-UKF), which combines a transformer-based model of system dynamics with analytically known sensor models via an UKF.<n>We evaluate FM-UKF on a new benchmark of container ship models with complex dynamics, demonstrating a competitive accuracy, effort, and trade-off compared to classical methods.
• Score: 12.83335441358014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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