An improved method for nonstationary signals components extraction based on the ICI rule
| Author | Lerga, J |
| Author | Sucic, V |
| Author | Boashash, B |
| Available date | 2012-06-18T08:16:03Z |
| Publication Date | 2011 |
| Publication Name | Signal Processing and their Applications (WOSSPA) |
| Citation | J. Lerga, V. Sucic, and B. Boashash, "An improved method for nonstationary signals components extraction based on the ICI rule," in Proc. of 2011 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA), 2011, pp. 307-310 |
| Description | This paper presents an automatic adaptive method to localize and extract signal components from a noisy multicomponent signal TFD. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated). |
| Abstract | This paper proposes an improved adaptive algorithm for components localization and extraction from a noisy multicomponent signal time-frequency distribution (TFD). The algorithm, based on the intersection of confidence intervals (ICI) rule, does not require any a priori knowledge of signal components and their mixture. Its efficiency is significantly enhanced by using high resolution and reduced cross-terms TFDs. The obtained results are compared for different signal-to-noise ratios (SNRs) and various time and lag window types used in the modified B-distribution (MBD) calculation, proving the method to be a valuable tool in noisy multicomponent signals components extraction in the time-frequency (TF) domain. |
| Language | en |
| Publisher | IEEE |
| Subject | adaptive signal detection ICI rule adaptive algorithm components localization intersection of confidence intervals modified B-distribution multicomponent signal nonstationary signals signal enhancement signals components extraction time-frequency distribution time-frequency domain |
| Type | Conference Paper |
Files in this item
This item appears in the following Collection(s)
-
Technology Innovation and Engineering Education Unit [56 items ]
Related items
Showing items related by title, author, creator and subject.
-
A Deep Learning Model for LoRa Signals Classification Using Cyclostationay Features
( IEEE Computer Society , 2021 , Conference Paper)With the witnessed exponential growth of Internet of Things (IoT) nodes deployment following the emerging applications, multiple variants of technologies have been proposed to handle the IoT requirements. Among the proposed ... -
Detection of seizure signals in newborns
( IEEE , 1999 , Conference Paper)This paper considers a system design for processing a multidimensional biomedical signal formed by EEG, ECG, EOG and motion recorded from a newborn, for the purpose of detection of epileptic seizures in newborns as an ... -
Multiple-view time-frequency distribution based on the empirical mode decomposition
( Institution of Engineering and Technology , 2010 , Article)This study proposes a novel, composite time-frequency distribution (TFD) constructed using a multiple-view approach. This composite TFD utilises the intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD) ...