Multipath is one of the dominant sources of error in highprecision GNSS applications. A tracking algorithm is presented that explicitely accounts for direct signal and multipath replicas in the model, in order to mitigate the contributions of the latter. A Bayesian approach has been taken, to infer some information from the time evolution model of the parameters. Due to the nonlinearity of the measurement model, a Particle Filtering algorithm has been designed. The proposed PF considers Rao-Blackwellization with a CKF and the selection of the importance density is performed via the use of Laplace's method, which yields to an importance density close the optimal. Simulations compare performance to EKF and PCRB.