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

Analysis of stochastic time series in N dimensions in the presence of strong measurement noise