  We propose a novel approach to input design for identification of nonlinear
state space models. The optimal input sequence is obtained by maximizing a
scalar cost function of the Fisher information matrix. Since the Fisher
information matrix is unavailable in closed form, it is estimated using
particle methods. In addition, we make use of Gaussian process optimization to
find the optimal input and to mitigate the problem of a large computational
cost incurred by the particle filter, as the method reduces the number of
functional evaluations. Numerical examples are provided to illustrate the
performance of the resulting algorithm.
