Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement
We propose a sensor placement method for spatio-temporal field estimation based on a kriged Kalman filter (KKF) using a network of static or mobile sensors. The developed framework dynamically designs the optimal constellation to place the sensors. We combine the estimation error (for the stationary...
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MDPI AG
2018-06-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/6/1778 |
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| author | Venkat Roy Andrea Simonetto Geert Leus |
| author_facet | Venkat Roy Andrea Simonetto Geert Leus |
| author_sort | Venkat Roy |
| collection | DOAJ |
| description | We propose a sensor placement method for spatio-temporal field estimation based on a kriged Kalman filter (KKF) using a network of static or mobile sensors. The developed framework dynamically designs the optimal constellation to place the sensors. We combine the estimation error (for the stationary as well as non-stationary component of the field) minimization problem with a sparsity-enforcing penalty to design the optimal sensor constellation in an economic manner. The developed sensor placement method can be directly used for a general class of covariance matrices (ill-conditioned or well-conditioned) modelling the spatial variability of the stationary component of the field, which acts as a correlated observation noise, while estimating the non-stationary component of the field. Finally, a KKF estimator is used to estimate the field using the measurements from the selected sensing locations. Numerical results are provided to exhibit the feasibility of the proposed dynamic sensor placement followed by the KKF estimation method. |
| first_indexed | 2024-04-11T22:01:04Z |
| format | Article |
| id | doaj.art-1fea58c0bbb44ffb847425a0ff294814 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:01:04Z |
| publishDate | 2018-06-01 |
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| spelling | doaj.art-1fea58c0bbb44ffb847425a0ff2948142022-12-22T04:00:56ZengMDPI AGSensors1424-82202018-06-01186177810.3390/s18061778s18061778Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor PlacementVenkat Roy0Andrea Simonetto1Geert Leus2NXP Semiconductors, High Tech Campus 46, 5656 AE Eindhoven, The NetherlandsOptimisation and Control group, IBM Research Ireland, Dublin 15, IrelandFaculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The NetherlandsWe propose a sensor placement method for spatio-temporal field estimation based on a kriged Kalman filter (KKF) using a network of static or mobile sensors. The developed framework dynamically designs the optimal constellation to place the sensors. We combine the estimation error (for the stationary as well as non-stationary component of the field) minimization problem with a sparsity-enforcing penalty to design the optimal sensor constellation in an economic manner. The developed sensor placement method can be directly used for a general class of covariance matrices (ill-conditioned or well-conditioned) modelling the spatial variability of the stationary component of the field, which acts as a correlated observation noise, while estimating the non-stationary component of the field. Finally, a KKF estimator is used to estimate the field using the measurements from the selected sensing locations. Numerical results are provided to exhibit the feasibility of the proposed dynamic sensor placement followed by the KKF estimation method.http://www.mdpi.com/1424-8220/18/6/1778sparsitykrigingKalman filtersensor placementconvex optimization |
| spellingShingle | Venkat Roy Andrea Simonetto Geert Leus Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement Sensors sparsity kriging Kalman filter sensor placement convex optimization |
| title | Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement |
| title_full | Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement |
| title_fullStr | Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement |
| title_full_unstemmed | Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement |
| title_short | Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement |
| title_sort | spatio temporal field estimation using kriged kalman filter kkf with sparsity enforcing sensor placement |
| topic | sparsity kriging Kalman filter sensor placement convex optimization |
| url | http://www.mdpi.com/1424-8220/18/6/1778 |
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