Estimation of distributed processes using mobile spatially distributed sensors

In this paper we consider the problem of estimating a spatially distributed process described by a partial differential equation, which is also contaminated by a zero mean Gaussian noise. The estimation process is achieved using a set of spatially distributed mobile sensors. The objective is to provide sensor control policies that aim at improving the state estimate. The metric is taken to be the expected state estimation error. Using different spatial norms, two guidance policies are proposed. Simulation studies implementing and comparing the two proposed control policies are provided.