Multisensor methods to estimate thermal diffusivity

Abstract

Several methods for the estimation of thermal diffusivity are studied in this work. In many application scenarios, the thermal diffusivity is unknown and must be estimated in order to perform other estimation functions (e.g., tracking of the physical phenomenon, or solving other inverse problems like localization or sensor variance, etc.). In particular, we describe: 1) The use of minimization methods (the Golden Mean and Lagarias' simplex) to determine the thermal diffusivity coefficient which when used in a forward heat flow simulation results in the least (vector) distance between the sampled data and the simulated data. 2) The Maximum Likelihood Estimate for thermal diffusivity. 3) The Extended Kalman Filter to recover the thermal diffusivity. We apply these methods to the determination of thermal diffusivity in snow.