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Array processing = Kronecker product...

Benesty, Jacob.

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Array processing = Kronecker product beamforming /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Array processing/ by Jacob Benesty, Israel Cohen, Jingdong Chen.
Reminder of title:	Kronecker product beamforming /
Author:	Benesty, Jacob.
other author:	Cohen, Israel.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xi, 189 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Problem Formulation with Uniform Linear Arrays -- Beamforming with Uniform Linear Arrays -- Generalization with Uniform Linear Arrays -- Approach with Nonuniform Linear Arrays -- Approach with Rectangular Arrays -- Spatiotemporal Signal Enhancement.
Contained By:	Springer eBooks
Subject:	Beamforming - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-030-15600-8
ISBN:	9783030156008

Array processing = Kronecker product beamforming /
Benesty, Jacob.

Array processingKronecker product beamforming /[electronic resource] :by Jacob Benesty, Israel Cohen, Jingdong Chen. - Cham :Springer International Publishing :2019. - xi, 189 p. :ill., digital ;24 cm. - Springer topics in signal processing,v.181866-2609 ;. - Springer topics in signal processing ;v.18..

Introduction -- Problem Formulation with Uniform Linear Arrays -- Beamforming with Uniform Linear Arrays -- Generalization with Uniform Linear Arrays -- Approach with Nonuniform Linear Arrays -- Approach with Rectangular Arrays -- Spatiotemporal Signal Enhancement.

The focus of this book is on array processing and beamforming with Kronecker products. It considers a large family of sensor arrays that allow the steering vector to be decomposed as a Kronecker product of two steering vectors of smaller virtual arrays. Instead of directly designing a global beamformer for the original array, once the steering vector has been decomposed, smaller virtual beamformers are designed and separately optimized for each virtual array. This means the matrices that need to be inverted are smaller, which increases the robustness of the beamformers, and reduces the size of the observations. The book explains how to perform beamforming with Kronecker product filters using an unconventional approach. It shows how the Kronecker product formulation can be used to derive fixed, adaptive, and differential beamformers with remarkable flexibility. Furthermore, it demonstrates how fixed and adaptive beamformers can be intelligently combined, optimally exploiting the advantages of both. The problem of spatiotemporal signal enhancement is also addressed, and readers will learn how to perform Kronecker product filtering in this context.

ISBN: 9783030156008

Standard No.: 10.1007/978-3-030-15600-8doiSubjects--Topical Terms:

3453813
Beamforming
--Mathematical models.

LC Class. No.: TK7871.67.A77 / B46 2019

Dewey Class. No.: 621.382

Array processing = Kronecker product beamforming /
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245 1 0 $a Array processing $h [electronic resource] : $b Kronecker product beamforming / $c by Jacob Benesty, Israel Cohen, Jingdong Chen.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xi, 189 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer topics in signal processing, $x 1866-2609 ; $v v.18
505 0 $a Introduction -- Problem Formulation with Uniform Linear Arrays -- Beamforming with Uniform Linear Arrays -- Generalization with Uniform Linear Arrays -- Approach with Nonuniform Linear Arrays -- Approach with Rectangular Arrays -- Spatiotemporal Signal Enhancement.
520 $a The focus of this book is on array processing and beamforming with Kronecker products. It considers a large family of sensor arrays that allow the steering vector to be decomposed as a Kronecker product of two steering vectors of smaller virtual arrays. Instead of directly designing a global beamformer for the original array, once the steering vector has been decomposed, smaller virtual beamformers are designed and separately optimized for each virtual array. This means the matrices that need to be inverted are smaller, which increases the robustness of the beamformers, and reduces the size of the observations. The book explains how to perform beamforming with Kronecker product filters using an unconventional approach. It shows how the Kronecker product formulation can be used to derive fixed, adaptive, and differential beamformers with remarkable flexibility. Furthermore, it demonstrates how fixed and adaptive beamformers can be intelligently combined, optimally exploiting the advantages of both. The problem of spatiotemporal signal enhancement is also addressed, and readers will learn how to perform Kronecker product filtering in this context.
650 0 $a Beamforming $x Mathematical models. $3 3453813
650 1 4 $a Signal, Image and Speech Processing. $3 891073
700 1 $a Cohen, Israel. $3 1066726
700 1 $a Chen, Jingdong. $3 896873
710 2 $a SpringerLink (Online service) $3 836513
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830 0 $a Springer topics in signal processing ; $v v.18. $3 3453812
856 4 0 $u https://doi.org/10.1007/978-3-030-15600-8
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