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A noise-compensated long correlation matching method for AR spectral estimation of noisy signals

K.K Paliwal
1988 Signal Processing  
A noise-compensated long correlation matching (NCLCM) method is proposed for autoregressi~e ~AR) spectral estimation of the noisy AR signals.  ...  This method first computes the AR parameters from the high-order "(ule-Walker equations.  ...  AR parameters {ai} from an overdetermined set of the following (p+q) Yule-Walker equations which include both low-and high-order Yule-Walker equations: P akR([i-kl)=-R(i), i=l,2,...  ... 
doi:10.1016/0165-1684(88)90062-x fatcat:p3o7gd7fe5dovhpnofmpo4ahuq

Two-Dimensional Autoregressive Modelling Using Joint Second And Third Order Statistics And A Weighting Scheme

Sarah Lee, Tania Stathaki
2004 Zenodo  
Publication in the conference proceedings of EUSIPCO, Viena, Austria, 2004  ...  THE COMBINED METHOD In [1] , a method which combines the Yule-Walker system of equations and the Yule-Walker system of equations in the third-order statistical domain is used to estimate the 2-D AR model  ...  Yule-Walker system of equations and the Yule-Walker system of equations in the third-order statistical domain, as well as a method which uses the combination of the above systems.  ... 
doi:10.5281/zenodo.38618 fatcat:246nm2f2lve3jctlmjijf6pxty

Iterative Estimation Algorithm of Autoregressive Parameters

Kazys Kazlauskas, Jaunius Kazlauskas
2006 Informatica  
We use high order Yule-Walker equations, sequentially estimate the noise variance, and exploit these estimated variances for the bias correction.  ...  This paper presents an iterative autoregressive system parameter estimation algorithm in the presence of white observation noise.  ...  So, the difference of the high-order Yule-Walker estimate and the least squares estimate is proportional to the noise variance.  ... 
doi:10.15388/informatica.2006.133 fatcat:jnu6v542rzclfihq6pt2ebsa2i

Non-iterative Subspace-based Method for Estimating AR Model Parameters in the Presence of White Noise with Unknown Variance

Majdoddin Esfandiari, Sergiy A. Vorobyov, Mahmood Karimi
2019 2019 53rd Asilomar Conference on Signals, Systems, and Computers  
The basic idea of the ESS is to estimate the variance of the observation noise via solving a generalized eigenvalue problem, and then estimate the AR parameters using the estimated variance.  ...  A new non-iterative subspace-based method named extended subspace (ESS) method is developed.  ...  For example in [27] , the variance of observation noise is estimated by minimizing a cost function formed by high-order Yule-Walker equations, while the AR parameters are estimated via low-order Yule-Walker  ... 
doi:10.1109/ieeeconf44664.2019.9048977 dblp:conf/acssc/EsfandiariVK19 fatcat:wbxoq7vfdnhkzpzk653lw5ee7i

Pervasive false brain connectivity from electrophysiological signals [article]

Roberto Pascual-Marqui, Peter Achemann, Pascal Faber, Toshihiko Kinoshita, Kieko Kochi, Keiichiro Nichida, Masafumi Yoshimura
2021 biorxiv/medrxiv   pre-print
An estimation method that accounts for noise is based on an overdetermined system of high-order multivariate Yule-Walker equations, which give reduced variance estimators for the coupling coefficients  ...  In addition, simulations result are presented for a zero connectivity case with noisy observations, where the new method correctly reports no connectivity while classical analyses (as found in most software  ...  Kay (1980) , that the autoregressive coefficients estimated from the high order Yule-Walker equations have very high variance.  ... 
doi:10.1101/2021.01.28.428625 fatcat:hvqnb3xnxbh4tdoir6zfgo2bpm

Multichannel AR parameter estimation from noisy observations as an errors-in-variables issue

Julien Petitjean, Eric Grivel, William Bobillet, Patrick Roussilhe
2009 Signal, Image and Video Processing  
The proposed algorithm outperforms existing methods, especially for low signal-to-noise ratio and when the variances of the additive noise are not necessarily the same on each channel. 1.  ...  Hence, the parameter estimation consists of searching every diagonal block matrix that satisfies this property, of reiterating this search for a higher model order and then of extracting the solution that  ...  Another approach is the 'noise-compensated' Yule-Walker equations which however require the estimation of the additive-noise variance [9] .  ... 
doi:10.1007/s11760-009-0112-9 fatcat:22a5347nlvbshiayyelxcp7dam

Mammogram Analysis Using Two-Dimensional Autoregressive Models: Sufficient or Not? [chapter]

Sarah Lee, Tania Stathaki
2005 Lecture Notes in Computer Science  
a set of AR model coefficients is estimated using a method that combines both the Yule-Walker system of equations and the Yule-Walker system of equations in the third-order statistical domain.  ...  In this paper, the possibility of having the estimated set of AR model coefficients of the block containing the tumour as a unique set of AR model coefficients for the entire mammogram is looked into.  ...  A number of methods are available in the literature for estimating the AR model coefficients, including the Yule-Walker system of equations (YW) [1] , the Yule-Walker system of equations in the third-order  ... 
doi:10.1007/11553595_110 fatcat:b6fki6fu7zesrkqd7ombtmmeyi

Modeling with noises for inertial sensors

Kedong Wang, Shaofeng Xiong, Yong Li
2012 Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium  
Due to the existence of the MA items, the Yule-Walker equation constructed by the colored noise's autocovariances starts from the order higher than the order of MA or AR model, which prevents from further  ...  Since there are no MA items in the approximated AR model, the Yule-Walker equation can be constructed from the 1st-order of the colored noise's autocovariances, which is beneficial to improving the estimation  ...  In the method, the ARMA model is approximated to a high-order AR model so that the low-order measurement autocovariances can be used to form the Yule-Walker equation.  ... 
doi:10.1109/plans.2012.6236937 fatcat:oact236r6jgupfmwzmr74louyy

Finite sample criteria for autoregressive order selection

P.M.T. Broersen
2000 IEEE Transactions on Signal Processing  
This leads to finite sample criteria for order selection that depend on the estimation method.  ...  Only the expectation of the logarithm of the residual energy, as a function of the model order, has been the basis for the previous classes of asymptotical and finite sample criteria.  ...  More general, the further estimates of higher orders after a large reflection coefficient suffer from a serious bias in the Yule-Walker method, instead of the smaller bias with magnitude that is present  ... 
doi:10.1109/78.887047 fatcat:xjvwqhor7jaldpg6lwe6n7jf6y

Phase-Rectified Signal Averaging Method Applied To Heart Rate Variability Signals For Assessment Of The Changes In Sympathovagal Balance During Rest And Tilt

Olivier Buttelli, Meryem Jabloun, Philippe Ravier
2010 Zenodo  
Publication in the conference proceedings of EUSIPCO, Aalborg, Denmark, 2010  ...  Figure 5 : 5 Deflection for different SNR: (-⋄) PRSA, (⋆) Yule-Walker AR spectral method with a model order fixed equal to 7 and (o) Yule-Walker AR spectral method with a model order selected by MDL, (  ...  This factor is about 2 when the LF/HF ratio is estimated by the Yule-Walker AR spectral method with a fixed model order equal to 7.  ... 
doi:10.5281/zenodo.42015 fatcat:42rimbg2xzeo5jn3yvnqeknaxe

Unbiased blind adaptive channel identification and equalization

D. Gesbert, P. Duhamel
2000 IEEE Transactions on Signal Processing  
We present a low-cost algorithm that solves this problem and allows the adaptive estimation of unbiased linear predictors in additive noise with arbitrary autocorrelation.  ...  This algorithm does not require the knowledge of the noise variance and relies on a new constrained prediction cost function. The technique can be applied in other noisy prediction problems.  ...  Unser (National Institutes of Health, Bethesda, MD) are gratefully acknowledged. The authors would also like to thank the anonymous reviewers for their comments.  ... 
doi:10.1109/78.815485 fatcat:aq7ffbshyncmfj6ar3ejh2cnaq

Noise Induces Biased Estimation of the Correction Gain

Jooeun Ahn, Zhaoran Zhang, Dagmar Sternad, Ramesh Balasubramaniam
2016 PLoS ONE  
We derive an analytical form of the bias from a simple regression method (Yule-Walker) and develop an improved identification method.  ...  This study reveals this limitation of current system identification methods and proposes a new method that overcomes this limitation.  ...  We analytically derived the bias in the estimates of the correction gain using the Yule-Walker equation [39, 40] , one of the simplest regression methods.  ... 
doi:10.1371/journal.pone.0158466 pmid:27463809 pmcid:PMC4963101 fatcat:ueeb67ru6neyxcut77zafyrcj4

Parameter Estimation of Fractional Low Order Time-frequency Autoregressive Based on Infinite Variance Analysis

Cao Ying, Yuan Qingshan, Zeng Lili
2015 Open Automation and Control Systems Journal  
order timefrequency autoregressive(FLO-TFAR) model and the concept of generalized TF-Yule-Walker equation are proposed, fractional low-order covariance is instead of autocorrelation in the model, The  ...  The detailed comparison of the FLO-TFAR S Sα model based on fractional low order moment(FLOM) and the Gaussian TFAR model based on autocorrelation.  ...  Because ( ) e n is a nonstationary process and it's variance is time-varying, TF-Yule-Walker equations and parameter estimationWhen the traditional Yule -Walker equations is solved, the TFAR model coefficient  ... 
doi:10.2174/1874444301507012083 fatcat:chkbigpywfghxnp2fqgo63dx74

Autoregressive spectral estimation [chapter]

Emanuel Parzen
1983 Handbook of Statistics  
The Yule-Walker equations are solved to estimate innovation variances im, to which are applied order determining criteria (AIC, CAT) to determine optimal orders m and also to test for white noise.  ...  A general form of non-parametric estimator is the kernel estimator -Durbin (1960) derive recursive methods of solving Yule-Walker equations which subsequently lead to fast algorithms for calculation  ...  Hannan and Quinn (1979) derive a modification of AIC which provides consistent estimators of the AR order, when exact model is assumed to be a finite order AR.  ... 
doi:10.1016/s0169-7161(83)03013-8 fatcat:s3533sd5xna3baknuicd2wun4i

Finite-Sample Bias Propagation in the Yule-Walker Method of Autoregressive Estimation

Piet M.T. Broersen
2008 IFAC Proceedings Volumes  
However, using them to compute an autoregressive model with the Yule-Walker method can give a strongly distorted spectral model in finite samples.  ...  Lagged-product autocorrelation estimates have a small triangular bias.  ...  That theory is based on approximations for the variance of reflection coefficients estimated from finite samples of a white noise process.  ... 
doi:10.3182/20080706-5-kr-1001.00462 fatcat:xnbzpspqmzfdlgjxx2mi5fazqi
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