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.

翻译：控制与系统工程中的状态估计传统上需要大量人工系统辨识或数据采集工作。然而，其他领域中基于Transformer的基础模型通过利用预训练的通用模型降低了对数据的需求。最终，开发系统动力学的零样本基础模型可大幅减少人工部署成本。尽管近期研究表明基于Transformer的端到端方法能在未见系统上实现零样本性能，但其仅限于训练过程中见过的传感器模型。本文提出基础模型无迹卡尔曼滤波（FM-UKF），该方法通过无迹卡尔曼滤波将基于Transformer的系统动力学模型与解析已知的传感器模型相结合，从而无需针对新传感器配置重新训练即可实现不同动力学场景的泛化。我们在具有复杂动力学的集装箱船模型新基准上评估FM-UKF，结果表明：相较于具有近似系统知识的经典方法以及端到端方法，FM-UKF在精度、计算成本和鲁棒性之间取得了更具竞争力的平衡。该基准与数据集已开源，以进一步支持通过基础模型实现零样本状态估计的未来研究。