A parallel sensor selection technique for identification of distributed parameter systems subject to correlated observations

Abstract

The paper considers the problem of determining optimal sensors locations so as to estimate unknown parameters in a class of distributed parameter systems when the measurement errors are correlated. Given a finite set of possible sensor positions, the problem is formulated as the selection of the gaged sites so as to maximize the log-determinant of the Fisher information matrix associated with the estimated parameters. The search for the optimal solution is performed using a GRASP method combined with a multipoint exchange algorithm. In order to alleviate the problem of excessive computational costs for large-scale problems, a parallel version of the GRASP solver is developed aimed at computations on a Linux cluster of PCs. The resulting numerical scheme is validated on a simulation example. © 2008 Springer-Verlag Berlin Heidelberg.