Robust sensor scheduling via iterative design for parameter estimation of distributed systems

The problem of selection of measurement data provided by sensor array so as to maximize the accuracy of parameter estimation of a distributed system defined in a given multidimensional domain is discussed. Usually, when designing an identification experiment for nonlinear models, the uncertainty of nominal parameters has to be taken into account. In particular, an iterative scheme for parameter estimation is proposed enhanced with sequential experimental design techniques where there is no particular information about the parameter distribution. The setting examined here correspond to situation where from among the observations provided by nodes of given sensor array the most informative measurements have to be selected in order to provide an update of the parameter estimates. Finally, a proposed approach is verified by a computer simulation regarding heat transfer problem.