Multichannel autoregressive spectral estimation from noisy observations

Hasan, Kamrul; Hossain, Junayed

doi:10.1109/tencon.2000.893683

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…It is known that the noisy process y(n) can be more accurately characterised by a higher-order AR process containing both noise and system poles [5]. Then this mixture of noise plus system poles can be used as candidate solutions for (17) and the desired system poles are to be extracted as the ones that minimise the cost function J j : Using a higher-order AR model and then extracting the desired roots based on a root matching technique has also been reported in [10] for autoregressive spectral estimation from noisy observations. In contrast with the main aim of computational complexity reduction, we first estimate poles of a higher-order AR model fitted to the observed noisy process by using the stable noise-uncompensated lattice filter (NULF) [19].…”

Section: Estimation Of Ar Parametersmentioning

confidence: 99%

Autocorrelation model-based identification method for ARMA systems in noise

Hasan

Hossain

Naylor

2005

IEE Proc., Vis. Image Process.

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A novel method for parameter estimation of minimum-phase autoregressive moving average (ARMA) systems in noise is presented. The ARMA parameters are estimated using a damped sinusoidal model representation of the autocorrelation function of the noise-free ARMA signal. The AR parameters are obtained directly from the estimates of the damped sinusoidal model parameters with guaranteed stability. The MA parameters are estimated using a correlation matching technique. The simulation results show that the proposed method can estimate the ARMA parameters with better accuracy as compared to other reported methods, in particular for low SNRs.

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Section: Estimation Of Ar Parametersmentioning

confidence: 99%

Autocorrelation model-based identification method for ARMA systems in noise

Hasan

Hossain

Naylor

2005

IEE Proc., Vis. Image Process.

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“…As for example, in case of a fourth-order system with two real poles and a pair of complex-conjugate poles, we need three steps. Once the poles are estimated, the AR system parameters can be obtained from their unique relationship [6].…”

Section: B Ar Parameter Estimation Using Damped Sinusoidal Modelmentioning

confidence: 99%

“…The most popular stochastic signal model is the Gaussian, minimum phase, AR model. In time-series analysis and signal modeling, both noise-free and noisy autoregressive (AR) systems have been extensively studied by many researchers [3]- [6]. In the latter case, except very few exceptions for colored noise, research results reported so far mostly considered white additive noise.…”

Section: Introductionmentioning

confidence: 99%

Identification of autoregressive signals in colored noise using damped sinusoidal model

Hasan

Chowdhury

Khan

2003

IEEE Trans. Circuits Syst. I

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This brief addresses a new method for autoregressive (AR) parameter estimation from colored noise-corrupted observations using a damped sinusoidal model for autocorrelation function of the noise-free signal. The damped sinusoidal model parameters are first estimated using a least-squares based method from the given noisy observations. The AR parameters are then directly obtained from the damped sinusoidal model parameters. The performance of the proposed scheme is evaluated using numerical examples.

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“…An approach that is somewhat similar to our approach is bias-corrected RLC (BCRLS) [ 56 , 57 , 58 , 59 , 60 , 61 ], but the BRCLS does not employ the covariance matrix-weighted least squares used here. Other approaches to noisy AR parameter estimation have been investigated in References [ 4 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ]…”

Section: Introductionmentioning

confidence: 99%

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Moon

Gunther

2020

Entropy

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Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation.

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Multichannel autoregressive spectral estimation from noisy observations

Cited by 4 publications

References 12 publications

Autocorrelation model-based identification method for ARMA systems in noise

Autocorrelation model-based identification method for ARMA systems in noise

Identification of autoregressive signals in colored noise using damped sinusoidal model

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

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