Spatio-temporal field estimation using kriged kalman filter (KKF) with sparsity-enforcing sensor placement

Spatio-temporal field estimation using kriged kalman filter (KKF) with sparsity-enforcing sensor placement

Roy, V. (NXP Semiconductors)
Simonetto, A. (IBM Research Ireland)
Leus, G.J.T. (TU Delft Signal Processing Systems)

2018

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.

Convex optimization
Kalman filter
Kriging
Sensor placement
Sparsity

http://resolver.tudelft.nl/uuid:4b472a6e-68db-4217-a2c8-e9f10ad9bd10

https://doi.org/10.3390/s18061778

1424-8220

Sensors, 18 (6), 1-20

Institutional Repository

journal article

© 2018 V. Roy, A. Simonetto, G.J.T. Leus