Polynomial subspace decomposition for broadband angle of arrival estimation

Mohamed Abubaker Alrmah; Jamie Corr; Ahmed Alzin; Keith Thompson; Stephan Weiss

doi:10.1109/SSPD.2014.6943305

Polynomial subspace decomposition for broadband angle of arrival estimation

Mohamed Abubaker Alrmah, Jamie Corr, Ahmed Alzin, Keith Thompson, Stephan Weiss

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

8 Citations (Scopus)

143 Downloads (Pure)

Abstract

In this paper we study the impact of polynomial or broadband subspace decompositions on any subsequent processing, which here uses the example of a broadband angle of arrival estimation technique using a recently proposed polynomial MUSIC (P-MUSIC) algorithm. The subspace decompositions are performed by iterative polynomial EVDs, which differ in their approximations to diagonalise and spectrally majorise s apce-time covariance matrix.We here show that a better diagonalisation has a significant impact on the accuracy of defining broadband signal and noise subspaces, demonstrated by a much higher accuracy of the P-MUSIC spectrum.

Original language	English
Title of host publication	2014 Sensor Signal Processing for Defence (SSPD)
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Print)	978-1-4799-5294-6
DOIs	https://doi.org/10.1109/SSPD.2014.6943305
Publication status	Published - Sept 2014
Event	2014 Sensor Signal Processing for Defence - Scotland, Edinburgh, United Kingdom Duration: 8 Sept 2014 → 9 Sept 2014

Conference

Conference	2014 Sensor Signal Processing for Defence
Country/Territory	United Kingdom
City	Edinburgh
Period	8/09/14 → 9/09/14

Keywords

covariance matrices
iterative methods
matrix decomposition
polynomial approximation
broadband signal

Access to Document

10.1109/SSPD.2014.6943305

Alrmah-etal-SSPD-2014-Polynomial-subspace-decomposition-for-broadband-angleAccepted author manuscript, 197 KB

Cite this

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title = "Polynomial subspace decomposition for broadband angle of arrival estimation",

abstract = "In this paper we study the impact of polynomial or broadband subspace decompositions on any subsequent processing, which here uses the example of a broadband angle of arrival estimation technique using a recently proposed polynomial MUSIC (P-MUSIC) algorithm. The subspace decompositions are performed by iterative polynomial EVDs, which differ in their approximations to diagonalise and spectrally majorise s apce-time covariance matrix.We here show that a better diagonalisation has a significant impact on the accuracy of defining broadband signal and noise subspaces, demonstrated by a much higher accuracy of the P-MUSIC spectrum.",

keywords = "covariance matrices, iterative methods, matrix decomposition, polynomial approximation, broadband signal",

author = "Alrmah, {Mohamed Abubaker} and Jamie Corr and Ahmed Alzin and Keith Thompson and Stephan Weiss",

year = "2014",

month = sep,

doi = "10.1109/SSPD.2014.6943305",

language = "English",

isbn = "978-1-4799-5294-6",

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note = "2014 Sensor Signal Processing for Defence ; Conference date: 08-09-2014 Through 09-09-2014",

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T1 - Polynomial subspace decomposition for broadband angle of arrival estimation

AU - Alrmah, Mohamed Abubaker

AU - Corr, Jamie

AU - Alzin, Ahmed

AU - Thompson, Keith

AU - Weiss, Stephan

PY - 2014/9

Y1 - 2014/9

N2 - In this paper we study the impact of polynomial or broadband subspace decompositions on any subsequent processing, which here uses the example of a broadband angle of arrival estimation technique using a recently proposed polynomial MUSIC (P-MUSIC) algorithm. The subspace decompositions are performed by iterative polynomial EVDs, which differ in their approximations to diagonalise and spectrally majorise s apce-time covariance matrix.We here show that a better diagonalisation has a significant impact on the accuracy of defining broadband signal and noise subspaces, demonstrated by a much higher accuracy of the P-MUSIC spectrum.

AB - In this paper we study the impact of polynomial or broadband subspace decompositions on any subsequent processing, which here uses the example of a broadband angle of arrival estimation technique using a recently proposed polynomial MUSIC (P-MUSIC) algorithm. The subspace decompositions are performed by iterative polynomial EVDs, which differ in their approximations to diagonalise and spectrally majorise s apce-time covariance matrix.We here show that a better diagonalisation has a significant impact on the accuracy of defining broadband signal and noise subspaces, demonstrated by a much higher accuracy of the P-MUSIC spectrum.

KW - covariance matrices

KW - iterative methods

KW - matrix decomposition

KW - polynomial approximation

KW - broadband signal

UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6943305

U2 - 10.1109/SSPD.2014.6943305

DO - 10.1109/SSPD.2014.6943305

M3 - Chapter (peer-reviewed)

SN - 978-1-4799-5294-6

SP - 1

EP - 5

BT - 2014 Sensor Signal Processing for Defence (SSPD)

PB - IEEE

T2 - 2014 Sensor Signal Processing for Defence

Y2 - 8 September 2014 through 9 September 2014

ER -

Polynomial subspace decomposition for broadband angle of arrival estimation

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Signal Processing Solutions for the Networked Battlespace

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Polynomial subspace decomposition for broadband angle of arrival estimation

Abstract

Conference

Keywords

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Signal Processing Solutions for the Networked Battlespace

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