1. A method for estimating hidden channel parameters comprising delay, amplitude and 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 a likelihood value of a measured channel output signal and updates the likelihood value using a state transition model, said method comprising utilizing previous knowledge about statistic dependencies of subsequent sets of time-varying channel parameters by introducing a movement model approximated to an actual channel environment, wherein said movement model corresponds to a Markov process and forms the state transition model; conducting an a-posteriori prediction based on knowledge that a reflection signal typically has a life cycle which starts from a first appearance of the reflection signal and experiences a gradual change over time in the hidden channel parameters until the reflection signal disappears; and with the movement model as a basis, conducting an estimation of the channel parameters sequentially by means of the recursive Bayesian filter, a number of paths being included in the estimation, to obtain not only a fixed estimated value, but also a-posteriori probability density functions of the estimated channel parameters; wherein the channel is approximated by a stochastic movement model of the following structure: The channel is completely characterized by a direct path (index i=1) and up to N m −1 echo signal paths; At time instant k, each path described by index i has a complex amplitude a i,k and a relative delay τ i,k=τi,k −τ i,k-1 , where the relative delays of the echo signals can only have positive values The path delays follow a stochastic process: τ l,k =τ l,k-1 +α l,k-1 ·Δt+n τ , Δτ i,k =Δt i,k-1 +α i,k-1 ·Δt+n τ , i>1; Parameters α l,k describe the speed at which the path delays change and are given by the following equation: α i , k = ( 1 - 1 K ) ⁢ α i , k - 1 + n α The value and the phase of each path, described by the complex amplitudes a i,k , are eliminated by maximizing a likelihood function for given values Δτ i,k =τ i,k −τ i,k-1 with respect to a i,k which reduces the parameter space and reduces complexity; Every path may either be “on” or “off”, specified by the parameter e i,k ε{0≡“off”, 1≡“on”}; and wherein the stochastic movement model includes two Gaussian noise sources n τ and n α as well as a noise process for controlling the state transitions for the values e i,k and parameter K determines how quickly the values Δτ i,k can change, and Δt refers to the time span between the time instants k-1 and k.