Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Rao-Blackwellized particle filter for multiple target tracking
Information Fusion
Analysis of parallelizable resampling algorithms for particle filtering
Signal Processing
EURASIP Journal on Applied Signal Processing
Channel tracking using particle filtering in unresolvable multipath environments
EURASIP Journal on Applied Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Complexity analysis of the marginalized particle filter
IEEE Transactions on Signal Processing
An efficient Newton-type method for the computation of ML estimators in a uniform linear array
IEEE Transactions on Signal Processing
Second-order complex random vectors and normal distributions
IEEE Transactions on Signal Processing
Marginalized particle filters for mixed linear/nonlinear state-space models
IEEE Transactions on Signal Processing
Resampling algorithms and architectures for distributed particle filters
IEEE Transactions on Signal Processing
Hi-index | 0.00 |
Multipath is today still one of the most critical problems in satellite navigation, in particular in urban environments, where the received navigation signals can be affected by blockage, shadowing, and multipath reception. Latest multipath mitigation algorithms are based on the concept of sequential Bayesian estimation and improve the receiver performance by exploiting the temporal constraints of the channel dynamics. In this paper, we specifically address the problem of estimating and adjusting the number ofmultipath replicas that is considered by the receiver algorithm. An efficient implementation via a two-fold marginalized Bayesian filter is presented, in which a particle filter, grid-based filters, and Kalman filters are suitably combined in order to mitigate the multipath channel by efficiently estimating its time-variant parameters in a track-before-detect fashion. Results based on an experimentally derived set of channel data corresponding to a typical urban propagation environment are used to confirm the benefit of our novel approach.