Title :
Time-frequency analysis using AR models with variable forgetting factors
Author :
Cho, Y. ; Kim, S. ; Powers, E.
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA
Abstract :
A method for estimating time-frequency representations of nonstationary signals using recursive least squares (RLS) with variable forgetting factors is described. The variable forgetting factor is adapted to a nonstationary signal by an extended prediction error criterion which accounts for the nonstationarity of the signal. This method has good adaptability in the nonstationary situation and low variance in the stationary situation
Keywords :
least squares approximations; parameter estimation; signal processing; spectral analysis; autoregressive models; nonstationary signals; prediction error criterion; recursive least squares; spectrum estimation; time-frequency analysis; variable forgetting factors; Adaptive filters; Computer errors; Covariance matrix; Frequency estimation; Least squares approximation; Parametric statistics; Recursive estimation; Resonance light scattering; Signal design; Time frequency analysis;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location :
Albuquerque, NM
DOI :
10.1109/ICASSP.1990.116096
