  An extension and generalization of a recently presented approach for the
analysis of Langevin-type stochastic processes in the presence of strong
measurement noise is presented. For a stochastic process in N dimensions which
is superimposed with strong, exponentially correlated, Gaussian distributed,
measurement noise it is possible to extract the strength and the correlation
functions of the noise as well as polynomial approximations of the drift and
diffusion functions of the underlying process.
