Dynamic Multipath Estimation by Sequential Monte Carlo Methods

: A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops it builds upon complexity reduction concepts that previously have been applied within Maximum Likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both artificially generated random channels and high resolution channel impulse responses recorded during a measurement campaign.