Robust Broadband Adaptive Beamforming via Polynomial Eigenvalues

Redif, Soydan; McWhirter, J.G.; Baxter, Paul; Cooper, Thomas J.

doi:10.1109/oceans.2006.307113

Cited by 33 publications

(29 citation statements)

References 9 publications

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“…For such quantities, narrowband techniques such as EVD and SVD have been extended to polynomials, with applications in denoising-type [3] or decorrelating preprocessors [4], transmit and receive beamforming across broadband MIMO channels [5,6], broadband angle of arrival estimation [7], and for optimum subband partitioning of beamformers [8].…”

Section: Introductionmentioning

confidence: 99%

MVDR broadband beamforming using polynomial matrix techniques

Weiss

Bendoukha

Alzin

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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This paper presents initial progress on formulating minimum variance distortionless response (MVDR) broadband beamforming using a generalised sidelobe canceller (GSC) in the context of polynomial matrix techniques. The quiescent vector is defined as a broadband steering vector, and we propose a blocking matrix design obtained by paraunitary matrix completion. The polynomial approach decouples the spatial and temporal orders of the filters in the blocking matrix, and decouples the adaptive filter order from the construction of the blocking matrix. For off-broadside constraints the polynomial approach is simple, and more accurate and considerably less costly than a standard time domain broadband GSC.

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Section: Introductionmentioning

confidence: 99%

MVDR broadband beamforming using polynomial matrix techniques

Weiss

Bendoukha

Alzin

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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“…Narrowband techniques such as eigenvalue (EVD ) and singular value decomposition (SVD) have been extended to cover the polynomial matrix case [6,20,10]. Algorithms for a polynomial EVD [10,13,14] have been driving forward applications in denoising-type [12] or decorrelating preprocessors [7] for beamforming, transmit and receive beamforming across broadband MIMO channels [4,18], broadband angle of arrival estimation [2], and for optimum subband partitioning of beamformers [22].…”

Section: Introductionmentioning

confidence: 99%

Adaptive broadband beamforming with arbitrary array geometry

Alzin¹,

Coutts²,

Corr³

et al. 2015

2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

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This paper expands on a recent polynomial matrix formulation for a minimum variance distortionless response (MVDR) broadband beamformer. Within the polynomial matrix framework, this beamformer is a straightforward extension from the narrowband case, and offers advantages in terms of complexity and robustness particularly for off-broadside constraints. Here, we focus on arbitrary 3-dimensional array configurations of no particular structure, where the straightforward formulation and incorporation of constraints is demonstrated in simulations, and the beamformer accurately maintains its look direction while nulling out interferers.

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“…The PEVD extends the wide-ranging utility of the EVD from narrowband to broadband problems, and iterative PEVD algorithms have in the past found use in optimal subband [1] and multichannel coding [2]; channel coding [3], transmit and receive beamforming across broadband MIMO channel [4], [5], angle of arrival estimation [6]. It can also provide a preprocessing stage for beamforming by applying denoising [7], decorrelation [8] and optimum subband decompositions [9], or enable novel MVDR beamforming approaches [10].…”

Section: Introductionmentioning

confidence: 99%

Performance trade-offs in sequential matrix diagonalisation search strategies

Corr

Thompson

Weiss

et al. 2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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This version is available at https://strathprints.strath.ac.uk/57580/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-Recently a selection of sequential matrix diagonalisation (SMD) algorithms have been introduced which approximate polynomial eigenvalue decomposition of parahermitian matrices. These variants differ only in the search methods that are used to bring energy onto the zero-lag. Here we analyse the search methods in terms of their computational complexities for different sizes of parahermitian matrices which are verified through simulated execution times. Another important factor for these search methods is their ability to transfer energy. Simulations show that the more computationally complex search methods transfer a greater proportion of the off-diagonal energy onto the zero-lag over a selected range of parahermitian matrix sizes. Finally we compare the real time convergence of the search methods as part of their respective SMD algorithms. The real time convergence experiments indicate that despite taking a longer time to compute each iteration the more complex algorithms that transfer more energy converge faster in real time. I. INTRODUCTIONSequential matrix diagonalisation encompasses a family of iterative algorithms that can factorise a parahermitian matrix into an approximate polynomial matrix eigenvalue decomposition (PEVD). The PEVD extends the wide-ranging utility of the EVD from narrowband to broadband problems, and iterative PEVD algorithms have in the past found use in optimal subband [1] Parahermitian matrices arise e.g. by including an explicit lag τ into the space-time covariance

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Robust Broadband Adaptive Beamforming via Polynomial Eigenvalues

Cited by 33 publications

References 9 publications

MVDR broadband beamforming using polynomial matrix techniques

MVDR broadband beamforming using polynomial matrix techniques

Adaptive broadband beamforming with arbitrary array geometry

Performance trade-offs in sequential matrix diagonalisation search strategies

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