DocumentCode :
3116262
Title :
Online Detection of the Nature of Complex-Valued Signals
Author :
Vayanos, Phebe ; Goh, Su Lee ; Mandic, Danilo P.
Author_Institution :
Imperial Coll. London, London
fYear :
2006
fDate :
6-8 Sept. 2006
Firstpage :
173
Lastpage :
178
Abstract :
A novel method for on-line tracking of the changes in the nature of a complex-valued signal is proposed. This is achieved by analysing the time variation of the mixing parameter within a hybrid complex-valued nonlinear adaptive filter. The proposed hybrid filter consists of a combination of split- and fully-complex nonlinear gradient descent algorithms, whose outputs are mixed in a convex manner. A learning algorithm for this scheme is derived and the potential of such an approach for tracking of signal modality changes is highlighted. The potential of the proposed approach is supported by simulations on both a synthetic benchmark signal and on real-world radar data.
Keywords :
adaptive filters; filtering theory; gradient methods; nonlinear filters; signal detection; complex-valued nonlinear adaptive filter; complex-valued signal detection; fully-complex nonlinear gradient descent algorithm; learning algorithm; split-complex nonlinear gradient descent algorithm; time variation analysis; Adaptive filters; Backpropagation algorithms; Educational institutions; Machine learning algorithms; Neural networks; Neurons; Radar tracking; Signal processing; Signal processing algorithms; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on
Conference_Location :
Arlington, VA
ISSN :
1551-2541
Print_ISBN :
1-4244-0656-0
Electronic_ISBN :
1551-2541
Type :
conf
DOI :
10.1109/MLSP.2006.275543
Filename :
4053642
Link To Document :
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