A particle filtering tracking algorithm for GNSS synchronization using Laplace’s method

Multipath is one of the dominant sources of error in high- precision 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.