Multipath estimating tracking loops in advanced GNSS receivers with particle filtering

Abstract

This paper studies Bayesian filtering techniques applied to the design of advanced delay/phase tracking loops in GNSS receivers, which estimate not only direct path parameters but those of multipath. The analysis includes trade-off among realistic propagation channel models and the use of a realistic simulation framework. The proposed filtering technique implements Rao-Blackwellization of linear states and a particle filter for the nonlinear partition. Computer simulations highlight the superior performance of such method when compared to traditional DLL/PLL based schemes, approaching theoretical limits.

Nonlinear Bayesian Tracking Loops for Multipath Mitigation

“…It has been reported in [37] that the number of correlators (L) used in the PF plays an important role. For instance, in AWGN on the order of L = 11 correlators are required to obtain stable results.…”

An EM-based multipath interference mitigation in GNSS receivers

“…According to ( 5) and ( 7) , the estimation of the state vector (containing the direct LOS signal parameters) results in a nonlinear filtering problem that can be solved using, e.g., the extended Kalman filter (KF) or the particle filter (PF) in the absence of MP interferences. It has been recognized that the KF and PF-based GNSS signal tracking loops can be possibly implemented in the GNSS receiver [35,36] . However, in the presence of MP interferences, the statistical models used to solve the MP mitigation problem in the GNSS receiver depend on unknown time-varying model parameter vectors (containing the MP signal parameters) that need to be estimated jointly with the state vector.…”