Title :
Adaptive AR modeling in white Gaussian noise
Author :
Wu, Wen-Rong ; Chen, Po-Cheng
Author_Institution :
Dept. of Commun. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fDate :
5/1/1997 12:00:00 AM
Abstract :
Autoregressive (AR) modeling is widely used in signal processing. The coefficients of an AR model can be easily obtained with a least mean square (LMS) prediction error filter. However, it is known that this filter gives a biased solution when the input signal is corrupted by white Gaussian noise. Treichler (1979) suggested the γ-LMS algorithm to remedy this problem and proved that the mean weight vector can converge to the Wiener solution. In this paper, we develop a new algorithm that extends works of Vijayan et al. (1990), for adaptive AR modeling in the presence of white Gaussian noise. By theoretical analysis, we show that the performance of the new algorithm is superior to the γ-LMS filter. Simulations are also provided to support our theoretical results
Keywords :
Gaussian noise; adaptive filters; adaptive signal processing; autoregressive processes; convergence of numerical methods; filtering theory; least mean squares methods; prediction theory; white noise; γ-LMS algorithm; γ-LMS filter; AR model coefficients; LMS prediction error filter; Wiener solution; adaptive AR modeling; algorithm performance; autoregressive modeling; biased solution; convergence; input signal; least mean square; mean weight vector; signal processing; simulations; white Gaussian noise; Adaptive algorithm; Adaptive filters; Gaussian noise; Information filtering; Information filters; Least squares approximation; Nonlinear filters; Resonance light scattering; Signal processing algorithms; Statistics;
Journal_Title :
Signal Processing, IEEE Transactions on
