Two-dimensional autoregressive (2-D AR) model order estimation

Aksasse, Brahim; Radouane, L.

doi:10.1109/78.771063

Cited by 50 publications

(29 citation statements)

References 18 publications

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“…The 2D-AR model order is determined by Akaike information criterion (AIC) which as an extension of 1D-AR model case is given by 6 AIC͑k 1 , k 2 ͒ = log͑ 2 ͒ +2k 1 k 2 / ͑MN͒ in which 2 is the prediction error variance. The results are shown in Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

Detection in reverberation using space time adaptive prewhiteners

Zhu

et al. 2008

The Journal of the Acoustical Society of America

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A major problem in moving platform active sonar systems is the detection of targets in spatially distributed and Doppler-spread reverberation. This paper presents a novel space time adaptive prewhitener for reverberation based on a two-dimensional autoregressive model. The space time adaptive prewhitener jointly processes received data in angle and Doppler to improve the separation of a target from reverberation. The detector using the space time adaptive prewhitener is shown to yield better detection performance than previously known schemes when operating in a reverberation background containing target echoes.

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Section: Simulation Resultsmentioning

confidence: 99%

Detection in reverberation using space time adaptive prewhiteners

Zhu

et al. 2008

The Journal of the Acoustical Society of America

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“…We may also use the minimum eigenvalue criterion (MEV), which is asymptotically equivalent to the BIC [34]. Since we can easily calculate white-noise variances σ 2 Gm |m = 1, 2, · · · using Algorithm A, we prefer the BIC to the MEV that needs eigenvalues of large matrices.…”

Section: -D Ar Spectrum Estimatementioning

confidence: 99%

Modeling 2-D AR Processes With Various Regions of Support

Choi

Politis

2007

IEEE Trans. Signal Process.

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“…Several methods for order selection exist based on information theoretic criteria, such as Akaike's information criterion (AIC) [2] and its variants, Akaike's final prediction error (FPE) [1], and the minimum description length (MDL) [10]. The fact that AIC is inconsistent has been well known a long time [11], whereas MDL is consistent [3]. An ARMA model order selection method based on recursive fuzzy reasoning for time-varying model identification was proposed in [6].…”

Section: Introductionmentioning

confidence: 99%

“…Liang's method depends on the minimum eigenvalue (MEV) of a covariance matrix computed from the observed data. The MEV criterion has been investigated in [3]- [5] and is shown to have an accuracy never before achieved.…”

Section: Introductionmentioning

confidence: 99%

ARMA Model Order Determination Using Edge Detection: A New Perspective

Al-Smadi

Al-Zaben

2005

Circuits Syst Signal Process

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An edge detection-based approach to estimate the order of an autoregressive moving average (ARMA) model process is presented. The proposed method performs edge detection to select the ARMA order by extracting the outlines of a data covariance matrix derived from the observed data sequence. The method is based on the minimum eigenvalue (MEV) criterion developed by Liang et al., IEEE Trans. Signal Process., 41(10): 3003-3009, 1993. The algorithm transforms the MEV covariance matrix into an image by normalizing and resizing the original covariance matrix. Then, a search is performed to locate changes in the intensity function, i.e., pixels where the brightness changes abruptly. Examples are presented to demonstrate the performance of this algorithm.

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Two-dimensional autoregressive (2-D AR) model order estimation

Cited by 50 publications

References 18 publications

Detection in reverberation using space time adaptive prewhiteners

Detection in reverberation using space time adaptive prewhiteners

Modeling 2-D AR Processes With Various Regions of Support

ARMA Model Order Determination Using Edge Detection: A New Perspective

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