Linear Aperiodic Array Synthesis Using an Improved Genetic Algorithm

A novel algorithm on beam pattern synthesis for linear aperiodic arrays with arbitrary geometrical configuration is presented in this paper. Linear aperiodic arrays are attractive for their advantages on higher spatial resolution and lower sidelobe. However, the advantages are attained at the cost o...

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Published inIEEE transactions on antennas and propagation Vol. 60; no. 2; pp. 895 - 902
Main Authors Cen, Ling, Yu, Zhu Liang, Ser, Wee, Cen, Wei
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.02.2012
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Online AccessGet full text
ISSN0018-926X
1558-2221
DOI10.1109/TAP.2011.2173111

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Abstract A novel algorithm on beam pattern synthesis for linear aperiodic arrays with arbitrary geometrical configuration is presented in this paper. Linear aperiodic arrays are attractive for their advantages on higher spatial resolution and lower sidelobe. However, the advantages are attained at the cost of solving a complex non-linear optimization problem. In this paper, we explain the Improved Genetic Algorithm (IGA) that simultaneously adjusts the weight coefficients and inter-sensor spacings of a linear aperiodic array in more details and extend the investigations to include the effects of mutual coupling and the sensitivity of the Peak Sidelobe Level (PSL) to steering angles. Numerical results show that the PSL of the synthesized beam pattern has been successfully lowered with the IGA when compared with other techniques published in the literature. In addition, the computational cost of our algorithm can be as low as 10% of that of a recently reported genetic algorithm based synthesis method. The excellent performance of IGA makes it a promising optimization algorithm where expensive cost functions are involved.
AbstractList A novel algorithm on beam pattern synthesis for linear aperiodic arrays with arbitrary geometrical configuration is presented in this paper. Linear aperiodic arrays are attractive for their advantages on higher spatial resolution and lower sidelobe. However, the advantages are attained at the cost of solving a complex non-linear optimization problem. In this paper, we explain the Improved Genetic Algorithm (IGA) that simultaneously adjusts the weight coefficients and inter-sensor spacings of a linear aperiodic array in more details and extend the investigations to include the effects of mutual coupling and the sensitivity of the Peak Sidelobe Level (PSL) to steering angles. Numerical results show that the PSL of the synthesized beam pattern has been successfully lowered with the IGA when compared with other techniques published in the literature. In addition, the computational cost of our algorithm can be as low as 10% of that of a recently reported genetic algorithm based synthesis method. The excellent performance of IGA makes it a promising optimization algorithm where expensive cost functions are involved.
Author Wee Ser
Wei Cen
Ling Cen
Zhu Liang Yu
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Issue 2
Keywords Performance evaluation
Lateral lobe
Mutual coupling
Peak Sidelobe Level (PSL)
beam pattern synthesis
Geometrical configuration
Nonlinear problems
linear arrays
Computational complexity
Optimization
Genetic Algorithms (GAs)
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Aperiodic arrays
Cost function
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Pattern synthesis
Antenna synthesis
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Snippet A novel algorithm on beam pattern synthesis for linear aperiodic arrays with arbitrary geometrical configuration is presented in this paper. Linear aperiodic...
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SubjectTerms Algorithms
Antennas
Aperiodic arrays
Applied sciences
Arrays
beam pattern synthesis
Beams (radiation)
Biological cells
Computational efficiency
Exact sciences and technology
Genetic algorithms
Genetic Algorithms (GAs)
linear arrays
Mutual coupling
Optimization
Optimization algorithms
Peak Sidelobe Level (PSL)
Radiocommunications
Sensor arrays
Sidelobes
Studies
Synthesis
Telecommunications
Telecommunications and information theory
Title Linear Aperiodic Array Synthesis Using an Improved Genetic Algorithm
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