Iterative approximation of analytic eigenvalues of a parahermitian matrix EVD

Stephan Weiss; Ian K. Proudler; Fraser K. Coutts; Jennifer Pestana

doi:10.1109/ICASSP.2019.8682407

Iterative approximation of analytic eigenvalues of a parahermitian matrix EVD

Stephan Weiss, Ian K. Proudler, Fraser K. Coutts, Jennifer Pestana

Research output: Contribution to conference › Paper › peer-review

17 Citations (Scopus)

57 Downloads (Pure)

Abstract

We present an algorithm that extracts analytic eigenvalues from a parahermitian matrix. Operating in the discrete Fourier transform domain, an inner iteration re-establishes the lost association between bins via a maximum likelihood sequence detection driven by a smoothness criterion. An outer iteration continues until a desired accuracy for the approximation of the extracted eigenvalues has been achieved. The approach is compared to existing algorithms.

Original language	English
Pages	8038-8042
Number of pages	5
DOIs	https://doi.org/10.1109/ICASSP.2019.8682407
Publication status	Published - 16 May 2019
Event	2019 International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019

Conference

Conference	2019 International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP 2019
Country/Territory	United Kingdom
City	Brighton
Period	12/05/19 → 17/05/19

Keywords

eigenvalues
parahermitian matrix
algorithm

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10.1109/ICASSP.2019.8682407

Weiss-etal-ICASSP-2019-Iterative-approximation-of-analytic-eigenvalues-of-a-parahermitian-matrix-EVDAccepted author manuscript, 332 KB

Cite this

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title = "Iterative approximation of analytic eigenvalues of a parahermitian matrix EVD",

abstract = "We present an algorithm that extracts analytic eigenvalues from a parahermitian matrix. Operating in the discrete Fourier transform domain, an inner iteration re-establishes the lost association between bins via a maximum likelihood sequence detection driven by a smoothness criterion. An outer iteration continues until a desired accuracy for the approximation of the extracted eigenvalues has been achieved. The approach is compared to existing algorithms.",

keywords = "eigenvalues, parahermitian matrix, algorithm",

author = "Stephan Weiss and Proudler, {Ian K.} and Coutts, {Fraser K.} and Jennifer Pestana",

note = "{\textcopyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 2019 International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",

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doi = "10.1109/ICASSP.2019.8682407",

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AU - Weiss, Stephan

AU - Proudler, Ian K.

AU - Coutts, Fraser K.

AU - Pestana, Jennifer

N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2019/5/16

Y1 - 2019/5/16

N2 - We present an algorithm that extracts analytic eigenvalues from a parahermitian matrix. Operating in the discrete Fourier transform domain, an inner iteration re-establishes the lost association between bins via a maximum likelihood sequence detection driven by a smoothness criterion. An outer iteration continues until a desired accuracy for the approximation of the extracted eigenvalues has been achieved. The approach is compared to existing algorithms.

AB - We present an algorithm that extracts analytic eigenvalues from a parahermitian matrix. Operating in the discrete Fourier transform domain, an inner iteration re-establishes the lost association between bins via a maximum likelihood sequence detection driven by a smoothness criterion. An outer iteration continues until a desired accuracy for the approximation of the extracted eigenvalues has been achieved. The approach is compared to existing algorithms.

KW - eigenvalues

KW - parahermitian matrix

KW - algorithm

UR - https://2019.ieeeicassp.org/

U2 - 10.1109/ICASSP.2019.8682407

DO - 10.1109/ICASSP.2019.8682407

M3 - Paper

SP - 8038

EP - 8042

T2 - 2019 International Conference on Acoustics, Speech, and Signal Processing

Y2 - 12 May 2019 through 17 May 2019

ER -

Iterative approximation of analytic eigenvalues of a parahermitian matrix EVD

Abstract

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Keywords

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Signal Processing in the Information Age (UDRC III)

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Iterative approximation of analytic eigenvalues of a parahermitian matrix EVD

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

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Signal Processing in the Information Age (UDRC III)

Cite this