Spatio-temporal sensor management for environmental field estimation.

We develop sparsity-enforcing spatio-temporal sensor management methods for environmental field monitoring applications. Leveraging the space-time stationarity, an environmental field can be estimated with a desired spatio-temporal resolution based on recorded measurements. If the field is non-stationary, it can be monitored dynamically based on the collected measurements and predictions made through a state model, if known a priori. We develop algorithms to implement sparse sensing, i.e., sensing only the most informative locations in space and time for both spatio-temporally stationary and non-stationary field monitoring applications. The selected sensing locations form an underdetermined measurement model which can be used to estimate the field based on the prior knowledge regarding the space-time variability of the field. The task of locating the most informative sensing locations can be performed for both multiple snapshots and a single snapshot based on the availability of prior knowledge (space-time correlation and dynamics) regarding the field, available computing power and the application. Centralized sensor placement problems for the estimation of both stationary and non-stationary fields are formulated as relaxed convex optimization problems, constrained by static or dynamic performance criteria. Finally, an iterative sparsity-enhancing saddle point method is formulated to solve both of these sensor placement problems. HighlightsSparsity-enforcing spatio-temporal sensor management methods are developed for environment monitoring, which can be implemented for spatio-temporally stationary as well as non-stationary field monitoring applications.A generalized sensor placement problem is formulated as relaxed convex optimization problem, constrained by static or dynamic performance criteria.An iterative sparsity-enhancing saddle point method is used to solve both of the sensor placement problems.