Path planning for data assimilation in mobile environmental monitoring systems

Abstract

By combining a low-order model of forecast errors, the extended Kalman filter, and classical continuous optimization, we develop an integrated methodology for planning mobile sensor paths to sample continuous fields. Agent trajectories are developed that specifically take into account the fact that data collected will be used for near real-time assimilation with large predictive models. This aspect of the problem has significant implications because the trajectories generated are very different from those which do not take the assimilation step into account, and their performance in controlling error is notably better.