title: Online detection of the modality of complex-valued real world signals.
abstract: A novel method for the online detection of the modality of complex-valued nonlinear and nonstationary signals is introduced. This is achieved using a convex combination of complex nonlinear adaptive filters with different transient characteristics. To facilitate the online mode of operation, the convex mixing parameter lambda within the proposed architecture is made gradient adaptive. Our focus is on the most important aspect of complex nonlinear modeling, that is, the identification of the split-complex and fully-complex nature of the signal in hand. The algorithms derived are robust and capable of tracking the changes in the modality of both benchmark and real world radar and wind complex vector fields.
authors.affiliation: Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, SW7 2AZ, London, United Kingdom. d.mandic@imperial.ac.uk
authors.forename: Danilo P
authors.initials: DP
authors.lastname: Mandic
authors.forename: Phebe
authors.initials: P
authors.lastname: Vayanos
authors.forename: Mo
authors.initials: M
authors.lastname: Chen
authors.forename: Su Lee
authors.initials: SL
authors.lastname: Goh
mesh_terms: D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009862:Online Systems; D012815:Signal Processing, Computer-Assisted