Nonlinear least-square solution to flat-top pattern synthesis using arbitrary linear array

Yuan Wen; Woon S. Gan; Jun Yang

doi:10.1016/j.sigpro.2005.04.008

Nonlinear least-square solution to flat-top pattern synthesis using arbitrary linear array

Yuan Wen^*, Woon S. Gan, Jun Yang

^*Corresponding author for this work

Nanyang Technological University

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

Abstract

This paper presents a new approach for synthesizing flat-top patterns, based on the least-square error criterion. The cost function is formulated according to the amplitude approximation error without phase constraint. The optimal array weight is obtained by using the Levenberg-Marquardt nonlinear optimization algorithm. Simulations are performed to compare the proposed approach with the Woodward-Lawson method and two recent methods using minimax and adaptive array techniques, respectively. The results indicate that the approach is effective in sidelobe control and synthesizing prespecified patterns for arbitrary linear arrays.

Original language	English
Pages (from-to)	1869-1874
Number of pages	6
Journal	Signal Processing
Volume	85
Issue number	9
DOIs	https://doi.org/10.1016/j.sigpro.2005.04.008
Publication status	Published - Sept 2005
Externally published	Yes

ASJC Scopus Subject Areas

Control and Systems Engineering
Software
Signal Processing
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Keywords

Array pattern synthesis
Least-square error
Nonlinear optimization

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10.1016/j.sigpro.2005.04.008

Cite this

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title = "Nonlinear least-square solution to flat-top pattern synthesis using arbitrary linear array",

abstract = "This paper presents a new approach for synthesizing flat-top patterns, based on the least-square error criterion. The cost function is formulated according to the amplitude approximation error without phase constraint. The optimal array weight is obtained by using the Levenberg-Marquardt nonlinear optimization algorithm. Simulations are performed to compare the proposed approach with the Woodward-Lawson method and two recent methods using minimax and adaptive array techniques, respectively. The results indicate that the approach is effective in sidelobe control and synthesizing prespecified patterns for arbitrary linear arrays.",

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T1 - Nonlinear least-square solution to flat-top pattern synthesis using arbitrary linear array

AU - Wen, Yuan

AU - Gan, Woon S.

AU - Yang, Jun

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N2 - This paper presents a new approach for synthesizing flat-top patterns, based on the least-square error criterion. The cost function is formulated according to the amplitude approximation error without phase constraint. The optimal array weight is obtained by using the Levenberg-Marquardt nonlinear optimization algorithm. Simulations are performed to compare the proposed approach with the Woodward-Lawson method and two recent methods using minimax and adaptive array techniques, respectively. The results indicate that the approach is effective in sidelobe control and synthesizing prespecified patterns for arbitrary linear arrays.

AB - This paper presents a new approach for synthesizing flat-top patterns, based on the least-square error criterion. The cost function is formulated according to the amplitude approximation error without phase constraint. The optimal array weight is obtained by using the Levenberg-Marquardt nonlinear optimization algorithm. Simulations are performed to compare the proposed approach with the Woodward-Lawson method and two recent methods using minimax and adaptive array techniques, respectively. The results indicate that the approach is effective in sidelobe control and synthesizing prespecified patterns for arbitrary linear arrays.

KW - Array pattern synthesis

KW - Least-square error

KW - Nonlinear optimization

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EP - 1874

JO - Signal Processing

JF - Signal Processing

IS - 9

ER -

Nonlinear least-square solution to flat-top pattern synthesis using arbitrary linear array

Abstract

ASJC Scopus Subject Areas

Keywords

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