Title :
Frequency-domain Volterra kernel estimation via higher-order statistical signal processing
Author :
Powers, E.J. ; Im, S. ; Kim, S.B. ; Tseng, C.-H.
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA
Abstract :
We discuss the utilization of higher-order spectral moments to determine frequency-domain Volterra kernels, given time series records of the random excitation and response of a nonlinear physical system. In particular, we consider frequency domain third-order Volterra kernel identification for nonGaussian excitation. Next an orthogonal Volterra like model valid for nonGaussian excitation is described. This model eliminates the interference terms associated with the nonorthogonal Volterra model, and thus facilitates decomposition of an observed power spectrum into its constituent linear quadratic, and cubic components
Keywords :
Gaussian processes; Volterra series; frequency estimation; frequency-domain analysis; higher order statistics; nonlinear systems; signal processing; time series; 3D frequency space; Volterra kernel estimation; cubic components; frequency-domain Volterra kernel estimation; higher-order spectral moments; higher-order statistical signal processing; linear components; nonGaussian excitation; nonlinear physical system response; nonorthogonal Volterra model; orthogonal Volterra like model; power spectrum decomposition; quadratic components; random excitation; third-order Volterra kernel identification; time series records; Frequency domain analysis; Frequency estimation; Interference elimination; Kernel; Nonlinear systems; Physics computing; Power engineering and energy; Power engineering computing; Power measurement; Power system modeling; Signal processing; System testing;
Conference_Titel :
Signals, Systems and Computers, 1993. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-4120-7
DOI :
10.1109/ACSSC.1993.342553
