Mobile Sensor Path Planning for Kalman Filter Spatiotemporal Estimation

Understand

The estimation of spatiotemporal data from limited sensor measurements is a required task across many scientific disciplines.

• The sensor selection problem, which aims to optimize the placement of sensors, leverages innovations in greedy algorithms and low-rank subspace projection to provide model-free, data-driven estimates.
• Alternatively, Kalman filter estimation balances model-based information and sparsely observed measurements to collectively make an estimation, with many related optimization algorithms developed for selecting optimal sensors.
• The majority of methods have been developed for stationary sensors, with relatively limited work estimating spatiotemporal data using mobile sensors that leverage both Kalman filtering and low-rank features.