Bayesian off-line segmentation applied to multi-carrier GPS signals fusion

This paper presents a Bayesian off-line fusion segmentation method, applied to the code tracking in a multi-carrier GPS receiver. The tracking is realized with discriminator values obtained on the different carrier frequencies. We suppose that the evolution of the pseudo-ranges satellites-receiver is piecewise linear. We propose a Bayesian method for the fusion of change detection models in the discriminators evolution. In this context, we construct a penalized contrast function to estimate the model parameters. The contrast function is deduced from log-likelihood of the parametric distribution that models the discriminators statistic evolution. We deduced the penalty term from the prior law of change instants. It is composed of parameters that guide the number of changes and of parameters that will bring prior information on the ionospheric delays between the GPS signals on the different carrier frequencies. We show on synthetic and real data the feasibility and the contribution of our method.