Method for estimating hidden channel parameters of a received GNNS navigation signal

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 Nm−1 echo signal paths;
• At time instant k, each path described by index i has a complex amplitude ai,k and a relative delay τi,k=τi,k−τi,k-1, where the relative delays of the echo signals can only have positive values
• The path delays follow a stochastic process:
• τl,k=τl,k-1+αl,k-1·Δt+nτ,
• Δτi,k=Δti,k-1+αi,k-1·Δt+nτ, i>1;
• Parameters αl,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 ai,k, are eliminated by maximizing a likelihood function for given values Δτi,k=τi,k−τi,k-1 with respect to ai,k which reduces the parameter space and reduces complexity;
• Every path may either be “on” or “off”, specified by the parameter ei,kε{0≡“off”, 1≡“on”};
• and
• wherein the stochastic movement model includes two Gaussian noise sources nτ and nα as well as a noise process for controlling the state transitions for the values ei,k and parameter K determines how quickly the values Δτi,k can change, and Δt refers to the time span between the time instants k-1 and k.