Bayesian Dll for Multipath Mitigation in Navigation Systems Using Particle Filters

Multipath estimating tracking loops in advanced GNSS receivers with particle filtering

“…p(x l n , x nl 0:n |y 1:n ) = p(x l n |x nl 0:n , y 1:n )p(x nl 0:n |y 1:n ) (32) and, taking into consideration that x l n generates a linear Gaussian state-space, p(x l n |x nl 0:n , y 1:n ) can be updated analytically via a KF conditional on x nl 0:n and only the non-linear part of x n needs to be estimated with a nonlinear filter. In the proposed scheme, a SPF is run to characterize p(x nl 0:n |y 1:n ) and a KF is executed to obtain p(x l n |x nl 0:n , y 1:n ).…”

“…Here the term Bayesian means that the algorithm is using some sort of a priori information regarding these parameters (such as interdependencies and time evolution models). This approach was first introduced in [32] and further refined in [33], although other papers might be found following the same scheme [34] with more complex time-evolving models. The application of Bayesian filtering techniques becomes straightforward when one describes the problem at hand in terms of a measurement equation and a process equation (i.e., how unknowns evolve randomly over time)…”

“…The evolution of τ m,n andτ m,n for the echoes is modeled with a truncated Gaussian distribution as in [32], which allows us to introduce the fact that due to physical reasons…”

A maximum likelihood-based unscented Kalman filter for multipath mitigation in a multi-correlator based GNSS receiver

“…The objective is then to estimate recursively the posterior probability density function of the unknown parameters associated with the LOS and MP signals. Considering that the GNSS measurements are related to the unknown parameters by highly nonlinear equations, the use of particle filters (PF) has been addressed in the literature [8][9][10]. Although many approaches have been suggested to improve the efficiency of these filters [11][12][13][14][15], a high computation still limits the real-time application of PF-based MP mitigation approaches.…”

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

“…Two important variations have been introduced w.r.t. [3]. Firstly, it considers a variance reduction technique, known as Rao-Blackwellization, that marginalizes linear parameters using a Complex Kalman Filter (CKF).…”