Blind identifiability of third-order Volterra nonlinear systems

TitleBlind identifiability of third-order Volterra nonlinear systems
Publication TypeConference Paper
Year of Publication2003
AuthorsTan, H. - Z., and T. Aboulnasr
Conference NameAcoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
PaginationVI - 665-8 vol.6
Date Publishedapr.
Keywordsadaptive signal processing, blind estimation, blind identification, computational complexity, higher order statistics, kernel parameters, LMS algorithms, nonlinear communication, nonlinear control, nonlinear signal processing, nonlinear systems, parameter estimation, persistent excitation, RLS algorithms, second order statistics, statistical analysis, third-order Volterra nonlinear systems, unobservable random sequence
Abstract

A novel approach to estimate blindly the kernels of a Volterra nonlinear system up to the third order is proposed. The system is excited by an unobservable i.i.d. random sequence. Blind identifiability is achieved using second order statistics (SOS) rather than using higher order statistical (HOS) information to ensure lower complexity. Since the output of the Volterra system is linearly dependent upon its kernel parameters, conventional LMS or RLS algorithms can be used and consistent estimation of Volterra kernels can be achieved provided some conditions of persistent excitation (PE) are satisfied. Simulation demonstrated the ability of the proposed method to achieve a good estimation performance.

URLhttp://dx.doi.org/10.1109/ICASSP.2003.1201769
DOI10.1109/ICASSP.2003.1201769

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