Performance of Discrete Wavelet Transform-based deconvolution applied to multipath channel estimation

Vaz, Canute; Daut, D.G.

doi:10.1109/sarnof.2012.6222726

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…For wavelet transformation, Haar's wavelet filters with one level of decomposition were used. This choice of wavelet was selected based on the deconvolution-based estimation results presented in Vaz and Daut (2012). The data collection setup is shown in Figure 7.…”

Section: Real Data Processing Resultsmentioning

confidence: 99%

Context-Aware Adaptive Multipath Compensation Based on Channel Pattern Recognition for GNSS Receivers

2017

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The possibility of identifying the type of multipath environment and receiver motion (e.g. pedestrian, vehicular) using pattern recognition approaches based on multipath parameters is investigated. This allows the receiver to adjust its tracking strategy and optimally tune its tracking parameters to mitigate code multipath effects. A Support Vector Machine (SVM) classification method with a modified Gaussian kernel is applied in this approach. A set of temporal and spectral features is extracted from the correlation samples of the received signals in different environments to train the classifier. The latter is then used in the structure of stochastic gradient-based adaptive multipath compensation and tracking techniques to tune the signal tracking parameters based on the environment and receiver motion. Simulation and real data measurements using Galileo E1B/C signals are performed to assess the validity of the proposed environment identification approaches and to evaluate the impact of the proposed context-based receiver parameter tuning techniques on tracking performance in multipath environments. Test results showed that the proposed classifiers have an accuracy between 86% and 92%, and the tracking performance improved by about 15%.

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Section: Real Data Processing Resultsmentioning

confidence: 99%