Cognitive kriging metamodels for forest characterization and target detection

The idea of applying the cognitive radar principle to the radar observations of forests is presented. It implies an adaptive design of experiments (DOE) that will allow us to construct probabilistic surrogate models with a known level of uncertainty. These models reduce significantly the computational cost, which is mandatory when running many numerical simulations in various configurations.