tailieunhanh - Báo cáo hóa học: " Research Article An Efficient Two-Fold Marginalized Bayesian Filter for Multipath Estimation in Satellite Navigation Receivers"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An Efficient Two-Fold Marginalized Bayesian Filter for Multipath Estimation in Satellite Navigation Receivers | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 287215 12 pages doi 2010 287215 Research Article An Efficient Two-Fold Marginalized Bayesian Filter for Multipath Estimation in Satellite Navigation Receivers Bernhard Krach 1 Patrick Robertson 2 and Robert Weigel3 1EADS Deutschland GmbH Rechliner Strafie 85077 Manching Germany 2 The Institute of Communications and Navigation at the German Aerospace Center DLR Oberpfaffenhofen 82234 Wessling Germany 3The Chair for Electronics Engineering at the University of Erlangen-Nuremberg Cauerstrafie 9 91058 Erlangen Germany Correspondence should be addressed to Bernhard Krach Received 27 March 2010 Revised 4 August 2010 Accepted 17 September 2010 Academic Editor Patrick Oonincx Copyright 2010 Bernhard Krach et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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 of multipath 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 .