Data Reconstruction Using Smart Sensor Placement
This paper deals with spatio-temporal field estimation with efficient sensor placement based on the QR decomposition. The proposed method also identifies the optimal number of sensors needed for field estimation that captures the most relevant features of the field of interest. To address the uncert...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/18/6008 |
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| author | Farnaz Boudaghi Danial Waleed Luis A. Duffaut Espinosa |
| author_facet | Farnaz Boudaghi Danial Waleed Luis A. Duffaut Espinosa |
| author_sort | Farnaz Boudaghi |
| collection | DOAJ |
| description | This paper deals with spatio-temporal field estimation with efficient sensor placement based on the QR decomposition. The proposed method also identifies the optimal number of sensors needed for field estimation that captures the most relevant features of the field of interest. To address the uncertainties inherent in spatio-temporal field estimation, a robust data-driven control method is utilized, providing resilience against unpredictable environmental and model changes. In particular, the approach uses the Kriged Kalman Filter (KKF) for uncertainty-aware field reconstruction. Unlike other reconstruction methods, the positional uncertainty originating from the data acquisition platform is integrated into the KKF estimator. Numerical results are presented to show the efficacy of the proposed dynamic sensor placement strategy together with the KKF field estimator. |
| format | Article |
| id | doaj-art-4528d45aa42148e5a3ffce5d6ceea062 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4528d45aa42148e5a3ffce5d6ceea0622025-08-20T01:55:51ZengMDPI AGSensors1424-82202024-09-012418600810.3390/s24186008Data Reconstruction Using Smart Sensor PlacementFarnaz Boudaghi0Danial Waleed1Luis A. Duffaut Espinosa2Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USADepartment of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USADepartment of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USAThis paper deals with spatio-temporal field estimation with efficient sensor placement based on the QR decomposition. The proposed method also identifies the optimal number of sensors needed for field estimation that captures the most relevant features of the field of interest. To address the uncertainties inherent in spatio-temporal field estimation, a robust data-driven control method is utilized, providing resilience against unpredictable environmental and model changes. In particular, the approach uses the Kriged Kalman Filter (KKF) for uncertainty-aware field reconstruction. Unlike other reconstruction methods, the positional uncertainty originating from the data acquisition platform is integrated into the KKF estimator. Numerical results are presented to show the efficacy of the proposed dynamic sensor placement strategy together with the KKF field estimator.https://www.mdpi.com/1424-8220/24/18/6008sensor placementQR placement methodoptimal number of sensorsmodel-free controlKriging Kalman Filter |
| spellingShingle | Farnaz Boudaghi Danial Waleed Luis A. Duffaut Espinosa Data Reconstruction Using Smart Sensor Placement Sensors sensor placement QR placement method optimal number of sensors model-free control Kriging Kalman Filter |
| title | Data Reconstruction Using Smart Sensor Placement |
| title_full | Data Reconstruction Using Smart Sensor Placement |
| title_fullStr | Data Reconstruction Using Smart Sensor Placement |
| title_full_unstemmed | Data Reconstruction Using Smart Sensor Placement |
| title_short | Data Reconstruction Using Smart Sensor Placement |
| title_sort | data reconstruction using smart sensor placement |
| topic | sensor placement QR placement method optimal number of sensors model-free control Kriging Kalman Filter |
| url | https://www.mdpi.com/1424-8220/24/18/6008 |
| work_keys_str_mv | AT farnazboudaghi datareconstructionusingsmartsensorplacement AT danialwaleed datareconstructionusingsmartsensorplacement AT luisaduffautespinosa datareconstructionusingsmartsensorplacement |