An adaptive approach for the identification of improper complex signals

Jelfs, B, Mandic, D and Douglas, S 2012, 'An adaptive approach for the identification of improper complex signals', Signal Processing, vol. 92, no. 2, pp. 335-344.


Document type: Journal Article
Collection: Journal Articles

Title An adaptive approach for the identification of improper complex signals
Author(s) Jelfs, B
Mandic, D
Douglas, S
Year 2012
Journal name Signal Processing
Volume number 92
Issue number 2
Start page 335
End page 344
Total pages 10
Publisher Elsevier
Abstract A real-time approach for the identification of second-order noncircularity (improperness) of complex valued signals is introduced. This is achieved based on a convex combination of a standard and widely linear complex adaptive filter, trained by the corresponding complex least mean square (CLMS) and augmented CLMS (ACLMS) algorithms. By providing a rigorous account of widely linear autoregressive modelling the analysis shows that the monitoring of the evolution of the adaptive convex mixing parameter within this structure makes it possible to both detect and track the complex improperness in real time, unlike current methods which are block based and static. The existence and uniqueness of the solution are illustrated through the analysis of the convergence of the convex mixing parameter. The analysis is supported by simulations on representative datasets, for a range of both proper and improper inputs.
Subject Signal Processing
Keyword(s) Augmented complex least mean square (ACLMS)
Collaborative filter
Complex circularity
Improper complex signals
Widely linear autoregressive modelling
Wind modelling
DOI - identifier 10.1016/j.sigpro.2011.07.020
Copyright notice © 2011 Elsevier B.V. All Rights Reserved.
ISSN 0165-1684
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 11 times in Scopus Article | Citations
Altmetric details:
Access Statistics: 10 Abstract Views  -  Detailed Statistics
Created: Thu, 11 May 2017, 13:36:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us