用大模型+卡尔曼滤波实现无需训练的系统状态估计
Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF
- 将Transformer动力学模型与已知传感器模型结合，通过无迹卡尔曼滤波融合
- 在货轮动态模型上达到与经典方法相当的精度和鲁棒性
- 首次实现不依赖重新训练即可适应新传感器配置的零样本状态估计
控制系统中的状态估计传统上需要大量手动系统辨识或数据采集。然而，其他领域基于Transformer的通用基础模型已通过预训练大幅降低数据需求。最终，构建系统动力学的零样本基础模型可显著减少部署工作量。尽管已有研究显示基于Transformer的端到端方法能在未见过的系统上实现零样本性能，但其仅限于训练中见过的传感器模型。本文提出基础模型无迹卡尔曼滤波器（FM-UKF），将基于Transformer的系统动力学模型与解析已知的传感器模型通过无迹卡尔曼滤波器结合，实现了对不同动力学的泛化，且无需为新传感器配置重新训练。我们在包含复杂动态的货轮模型新基准上评估了FM-UKF，结果表明其在精度、工作量与鲁棒性之间表现优于具有近似系统知识的经典方法及端到端方法。该基准与数据集已开源，以支持未来基于基础模型的零样本状态估计研究。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。