Bispectrum estimation using a MISO autoregressive model

Name: Bispectrum estimation using a MISO autoregressive model
Author: Erdem, Tanju, Ercan, Ali Özer

İsim	Bispectrum estimation using a MISO autoregressive model
Yazar	Erdem, Tanju, Ercan, Ali Özer
Basım Tarihi:	2016
Basım Yeri	- Springer International Publishing
Konu	Bispectrum estimation, Bicumulant sequence, MISO autoregressive system, System identification
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1863-1711
Kayıt Numarası	56086444-44c0-42f7-b787-fd56e2e16dcb
Lokasyon	Electrical & Electronics Engineering, Computer Science
Tarih	2016
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Bispectra are third-order statistics that have been used extensively in analyzing nonlinear and non-Gaussian data. Bispectrum of a process can be computed as the Fourier transform of its bicumulant sequence. It is in general hard to obtain reliable bicumulant samples at high lags since they suffer from large estimation variance. This paper proposes a novel approach for estimating bispectrum from a small set of given low lag bicumulant samples. The proposed approach employs an underlying MISO system composed of stable and causal autoregressive components. We provide an algorithm to compute the parameters of such a system from the given bicumulant samples. Experimental results show that our approach is capable of representing non-polynomial spectra with a stable underlying system model, which results in better bispectrum estimation than the leading algorithm in the literature.
DOI	10.1007/s11760-016-0888-3

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Bispectrum estimation using a MISO autoregressive model

Yazar Erdem, Tanju, Ercan, Ali Özer

Basım Tarihi 2016

Basım Yeri - Springer International Publishing

Konu Bispectrum estimation, Bicumulant sequence, MISO autoregressive system, System identification

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1863-1711

Kayıt Numarası 56086444-44c0-42f7-b787-fd56e2e16dcb

Lokasyon Electrical & Electronics Engineering, Computer Science

Tarih 2016

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin Bispectra are third-order statistics that have been used extensively in analyzing nonlinear and non-Gaussian data. Bispectrum of a process can be computed as the Fourier transform of its bicumulant sequence. It is in general hard to obtain reliable bicumulant samples at high lags since they suffer from large estimation variance. This paper proposes a novel approach for estimating bispectrum from a small set of given low lag bicumulant samples. The proposed approach employs an underlying MISO system composed of stable and causal autoregressive components. We provide an algorithm to compute the parameters of such a system from the given bicumulant samples. Experimental results show that our approach is capable of representing non-polynomial spectra with a stable underlying system model, which results in better bispectrum estimation than the leading algorithm in the literature.

DOI 10.1007/s11760-016-0888-3