Open AccessMulticomponent noisy signal adaptive instantaneous frequency estimation using components time support information
Author(s) -
Sucic Victor,
Lerga Jonatan,
Boashash Boualem
Publication year - 2014
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2013.0349
Subject(s) - instantaneous phase , computer science , algorithm , mean squared error , entropy (arrow of time) , time–frequency analysis , intersection (aeronautics) , signal (programming language) , pattern recognition (psychology) , noise (video) , mathematics , signal processing , artificial intelligence , statistics , computer vision , programming language , aerospace engineering , telecommunications , radar , physics , filter (signal processing) , quantum mechanics , engineering , image (mathematics)
This study proposes an adaptive method for components instantaneous frequency (IF) estimation of noisy non‐stationary multicomponent signals, combined with the components time‐support estimation method based on the short‐time Rényi entropy (STRE). Components localisation and separation are done using a double‐direction component tracking and extraction method presented here, while the IF estimation is done using the adaptive algorithms based on the intersection of confidence intervals (ICI) rule and the relative intersection of confidence intervals (RICI) rule. The results obtained using the ICI and RICI rules are compared for various window types, signal‐to‐noise ratios and time–frequency distributions, both with and without using the information on components time support. Most of the errors in IF estimation using the ICI and RICI‐based methods are caused by imprecise components time‐support estimation. The proposed methods combined with the STRE have achieved a significant accuracy improvement in terms of the mean absolute error and the mean squared error, reducing them by up to 73 and 93%, respectively. The method has been applied to real‐life signals and proven to be an efficient tool for IF estimation of noisy non‐stationary multicomponent signals.