Patent ID: 8265202
Filing Date: 2012-09-11
Classification: G01S

Abstract:
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 At time instant k, each path described by index i has a complex amplitude a The path delays follow a stochastic process: Parameters α The value and the phase of each path, described by the complex amplitudes a Every path may either be “on” or “off”, specified by the parameter e wherein the stochastic movement model includes two Gaussian noise sources n