A New Method of Noise Variance Estimation from Low-Order Yule-Walker Equations

Jonah GAMBA  Tetsuya SHIMAMURA 

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences  Vol.E87-A  No.1  pp.270-274
Publication Date: 2004/01/01
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Digital Signal Processing
Keyword: 
noise variance,  Yule-Walker equations,  autoregressive process,  subspace method, 

Full Text: PDF(162.9KB)

Summary: 
The processing of noise-corrupted signals is a common problem in signal processing applications. In most of the cases, it is assumed that the additive noise is white Gaussian and that the constant noise variance is either available or can be easily measured. However, this may not be the case in practical situations. We present a new approach to additive white Gaussian noise variance estimation. The observations are assumed to be from an autoregressive process. The method presented here is iterative, and uses low-order Yule-Walker equations (LOYWEs). The noise variance is obtained by minimizing the difference in the second norms of the noisy Yule-Walker solution and the estimated noise-free Yule-Walker solution. The noise-free solution is constrained to match the observed autocorrelation sequence. In the iterative noise variance estimation method, a variable step-size update scheme for the noise variance parameter is utilized. Simulation results are given to confirm the effectiveness of the proposed method.