Willems' Fundamental Lemma with Large Noisy Fragmented Dataset
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915904374177792 |
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| author | Kiani, Sahand Lagoa, Constantino M. |
| author_facet | Kiani, Sahand Lagoa, Constantino M. |
| contents | Willems' Fundamental Lemma enables parameterizing all trajectories generated by a Linear Time-Invariant (LTI) system directly from data. However, this lemma relies on the assumption of noiseless measurements. In this paper, we provide an approach that enables the applicability of Willems' Fundamental Lemma with a large noisy-input, noisy-output fragmented dataset, without requiring prior knowledge of the noise distribution. We introduce a computationally tractable and lightweight algorithm that, despite processing a large dataset, executes in the order of seconds to estimate the invariants of the underlying system, which is obscured by noise. The simulation results demonstrate the effectiveness of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00338 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Willems' Fundamental Lemma with Large Noisy Fragmented Dataset Kiani, Sahand Lagoa, Constantino M. Systems and Control Willems' Fundamental Lemma enables parameterizing all trajectories generated by a Linear Time-Invariant (LTI) system directly from data. However, this lemma relies on the assumption of noiseless measurements. In this paper, we provide an approach that enables the applicability of Willems' Fundamental Lemma with a large noisy-input, noisy-output fragmented dataset, without requiring prior knowledge of the noise distribution. We introduce a computationally tractable and lightweight algorithm that, despite processing a large dataset, executes in the order of seconds to estimate the invariants of the underlying system, which is obscured by noise. The simulation results demonstrate the effectiveness of the proposed method. |
| title | Willems' Fundamental Lemma with Large Noisy Fragmented Dataset |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.00338 |