1. A method for estimating hidden channel parameters, namely the delay, the amplitude and the phase of a GNSS navigation signal received in a dynamic multipath environment, using a sequential estimation by means of a recursive Bayesian filtering that starts from the likelihood value of the measured channel output signal and updates the same using a state transition model, wherein previous knowledge about the statistic dependences of subsequent sets of time-varying channel parameters is utilized by introducing a movement model approximated to the actual channel environment, which model corresponds to a Markov process and forms the state transition model, the knowledge that reflection signals typically have a life cycle starting from their first appearance and experiencing a gradual change in delay, amplitude and phase over time until they disappear, being utilized for an a-posteriori prediction, and, with this movement model as the basis, the channel parameters are estimated sequentially by means of the recursive Bayesian filter, the number of paths being implicitly included in the estimation, and the result of this estimation is not only a fixed estimated value, but yields a-posteriori probability density functions of the estimated channel parameters.