Analysis of linear prediction, coding, and spectral estimation from subbands

Rao, S.S.; Pearlman, William A.

doi:10.1109/18.508839

Cited by 57 publications

(33 citation statements)

References 17 publications

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“…본 연 구에서는 적응배열 안테나를 적용하고 추정 도래 방향 방법은 선형예측(Linear Prediction)방법을 사 용한다. 선형예측방법은 시간축상에서 사용된 자동 퇴행(Auto Regressive)방법을 공간축상에 적용한 것이다 [4]. 적응 …”

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A Study on Modified Linear Prediction Method to Improve Target Estimation

Lee¹,

Joo²

2016

The Journal of Korea Institute of Information, Electronics, and

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In this paper, we studied a modified linear prediction method to estimate target signal correctly. Linear prediction method estimate direction-of-arrival to linear combination for any antenna element and other antenna elements. Modified linear prediction used optimal weight and posterior probability method. Through simulation, we are comparative analysis about the performance of proposed, bartlett and MUSIC method. From simulation, Bartlett and MUSIC method was estimation 3 targets signal, and proposed method estimated 4 targets. We showed the superior performance of the proposed algorithm relative to the classical method in order to estimate of target signals.

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Section: 서론unclassified

A Study on Modified Linear Prediction Method to Improve Target Estimation

Lee¹,

Joo²

2016

The Journal of Korea Institute of Information, Electronics, and

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“…The subband approach may be seen as a pre-processing of the signal, and thus is independent from the method used for the estimation. In addition to their computational efficiency, these techniques present several other advantages comparatively to a fullband estimation [32,36,37]. In particular, it is known that data decimation (or downsampling) may increase the resolution capabilities of the estimator considered [38] and enables one to process signals at low signal-to-noise ratios [39].…”

Section: O S S I E Rmentioning

confidence: 99%

NMR Data Analysis: A Time-Domain Parametric Approach Using Adaptive Subband Decomposition

Djermoune

Tomczak

Brie

2013

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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and available online here Cet article fait partie du dossier thématique ci-dessous publié dans la revue OGST, Vol. 69, n°2, et téléchargeable ici Résumé -Analyse de données RMN : une approche paramétrique basée sur une décomposi-tion en sous-bandes adaptative -Dans ce papier, nous proposons une méthode rapide d'analyse de signaux de spectroscopie de Résonance Magnétique Nucléaire (RMN), dans le cas Lorentzien, fondée sur une décomposition adaptative en sous-bandes. Cette dernière est obtenue par une succession d'étages de filtrage/décimation aboutissant à un arbre de décomposition. À chaque noeud de l'arbre, les paramètres du signal correspondant à la sous-bande sont estimés par une méthode haute-résolution. Puis l'erreur d'estimation est utilisée dans un test d'arrêt qui permet de décider si la décomposition doit être poursuivie ou non dans cette branche. Ainsi, cette approche permet la sélection automatique du niveau de décimation ce qui conduit la décomposition à s'adapter au contenu fréquentiel du signal. De plus, la méthode entraîne une réduction du temps de calcul et facilite le choix des paramètres libres comparativement à une analyse globale du signal. Son efficacité est illustrée au travers d'exemples de signaux RMN 13 C 1-D et 2-D.

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“…It was shown by Rao and Pearlman [1] that the wellknown AR modeling was a promising method for spectrum estimation in subbands, and it was proved that pth-order prediction from subbands is superior to pthorder prediction in the fullband when p is finite, and subband decomposition of a source resulted in a whitening of the composite subband spectrum. The equivalence of linear prediction and AR spectrum estimation was then exploited to show that AR spectrum from subbands offers a gain over fullband AR spectrum estimation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multichannel sequential lattice prediction filtering method for ARMA spectrum estimation in subbands

Ozden

2013

EURASIP J. Adv. Signal Process.

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A multichannel characterization for autoregressive moving average (ARMA) spectrum estimation in subbands is considered in this article. The fullband ARMA spectrum estimation can be realized in two-channels as a special form of this characterization. A complete orthogonalization of input multichannel data is accomplished using a modified form of sequential processing multichannel lattice stages. Matrix operations are avoided, only scalar operations are used, and a multichannel ARMA prediction filter with a highly modular and suitable structure for VLSI implementations is achieved. Lattice reflection coefficients for autoregressive (AR) and moving average (MA) parts are simultaneously computed. These coefficients are then converted to process parameters using a newly developed Levinson-Durbin type multichannel conversion algorithm. Hence, a novel method for spectrum estimation in subbands as well as in fullband is developed. The computational complexity is given in terms of model order parameters, and comparisons with the complexities of nonparametric methods are provided. In addition, the performance is visually and statistically compared against those of the nonparametric methods under both stationary and nonstationary conditions.

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Analysis of linear prediction, coding, and spectral estimation from subbands

Abstract: Linear Prediction schemes make a predictionx i of a data sample x i using p previous samples. It has been shown 1, 3] that as the order of prediction p ! 1, there is no gain to be obtained by coding subband samples. This

Cited by 57 publications

References 17 publications

A Study on Modified Linear Prediction Method to Improve Target Estimation

A Study on Modified Linear Prediction Method to Improve Target Estimation

NMR Data Analysis: A Time-Domain Parametric Approach Using Adaptive Subband Decomposition

Adaptive multichannel sequential lattice prediction filtering method for ARMA spectrum estimation in subbands

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