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Method for synthesizing directional diagrams of linear antenna arrays on basis of wavelet mutation wind drive optimization algorithms

A technology for wind-driven optimization and pattern synthesis, applied in the field of array antennas

Active Publication Date: 2015-09-09
JIANGSU UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Similar to swarm intelligence algorithms such as particle swarm optimization algorithm, WDO algorithm also has a contradiction between global exploration ability and local development ability

Method used

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  • Method for synthesizing directional diagrams of linear antenna arrays on basis of wavelet mutation wind drive optimization algorithms
  • Method for synthesizing directional diagrams of linear antenna arrays on basis of wavelet mutation wind drive optimization algorithms
  • Method for synthesizing directional diagrams of linear antenna arrays on basis of wavelet mutation wind drive optimization algorithms

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Experimental program
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Effect test

Embodiment 1

[0094] Design requirements: the number of array elements is 2N=20, the distance between array elements is d=λ / 2, the excitation current phase is 0 (side-fire array), the main lobe is required to be aligned at 90°, and the first zero beam width is 2θ 0 =20°, the maximum side lobe level SLVL=-35dB, optimize the amplitude value of the excitation current, and choose f as the objective function 1 =η|MSLVL-SLVL|+λ(MBW-BW) 2 .

[0095] Parameter setting: set the maximum number of iterations T = 200, the number of air particles in the population m = 200, the particle dimension D = 10, the parameters in the WDO algorithm α = 0.1, g = 0.1, RT = 2.6, c = 0.4, wavelet mutation probability pm=0.2, wavelet mutation operator parameter ξ wm =15, g wm =1000, the maximum speed u max =0.25, objective function weight η=0.8, λ=0.2.

Embodiment 2

[0097] Design requirements: the number of array elements is 2N=20, the distance between array elements is d=λ / 2, the excitation current phase is 0 (side-fire array), the main lobe is required to be aligned at 90°, and the first zero beam width is 2θ 0 =20°, the maximum side lobe level SLVL=-20dB, form a null trap in the direction of 60° and 70°, design the null trap depth NLVL=-100dB, optimize the amplitude value of the excitation current, select the objective function f 2 =α|MSLVL-SLVL|+β|NULL-NLVL|+γ(MBW-BW) 2 .

[0098] Parameter setting: set the maximum number of iterations T = 1000, the number of air particles in the population m = 200, the particle dimension D = 10, the parameters in the WDO algorithm α = 0.1, g = 0.1, RT = 2.6, c = 0.4, wavelet mutation probability pm=0.2, wavelet mutation operator parameter ξ wm =15, g wm =1000, the maximum speed u max =0.25, objective function weight α=0.8, β=0.2, γ=1.

Embodiment 3

[0100] Design requirements: the number of array elements is 2N=20, the distance between array elements is d=λ / 2, the excitation current phase is 0 (side-fire array), the main lobe is required to be aligned at 90°, and the first zero beam width is 2θ 0 =20°, the maximum side lobe level SLVL=-20dB, form null traps in the directions of 50°, 60°, and 70°, design the null trap depth NLVL=-100dB, optimize the amplitude value of the excitation current, and select f as the objective function 2 =α|MSLVL-SLVL|+β|NULL-NLVL|+γ(MBW-BW) 2 .

[0101] Parameter setting: set the maximum number of iterations T = 1000, the number of air particles in the population m = 200, the particle dimension D = 10, the parameters in the WDO algorithm α = 0.1, g = 0.1, RT = 2.6, c = 0.4, wavelet mutation probability pm=0.2, wavelet mutation operator parameter ξ wm =15, g wm =1000, the maximum speed u max =0.25, objective function weight α=0.8, β=0.2, γ=1.

[0102] Using the wavelet variant wind-driven o...

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Abstract

The invention discloses a method for synthesizing directional diagrams of linear antenna arrays on the basis of wavelet mutation wind drive optimization algorithms. The method includes steps of building models of the linear antenna arrays and determining comprehensive radiation characteristic requirements and objective functions of the antenna arrays; determining the wind drive optimization algorithms and wavelet mutation operator parameters and setting population sizes, weight values of fitness functions and speeds and position boundaries of air particles; randomly generating initial speeds and positions of the air particles, substituting the positions of the air particles into the fitness functions, sorting fitness values according to ascending order, updating population sequences and determining the global optimal positions and the local optimal positions; updating the speeds and the positions of the air particles; selectively carrying out wavelet mutation on the positions of the air particles according to mutation probability; computing fitness values of the air particles at novel positions, sorting the fitness values according to ascending order again, updating the population sequences and updating the global optimal positions and the local optimal positions until the maximum number of iterations are carried out. The method has the advantages of high solving precision and convergence speed.

Description

technical field [0001] The invention relates to an antenna array pattern synthesis method, in particular to a linear antenna array pattern synthesis method based on a wavelet variable wind drive optimization algorithm, and belongs to the field of array antennas. Background technique [0002] The purpose of antenna array pattern synthesis is to determine the excitation amplitude, phase, or array element position of the array element, so that the far-field pattern of the array meets certain technical requirements, such as low sidelobe level and null trap formation in a given direction. However, most of the pattern synthesis problems of array antennas present the characteristics of multi-parameter, non-differentiable, and even discontinuous, and the optimization of pattern parameters is a nonlinear optimization problem. Traditional optimization techniques are mostly based on gradient optimization techniques or random search methods. Among them, the conjugate gradient method ha...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
Inventor 田雨波任作琳孙菲艳
Owner JIANGSU UNIV OF SCI & TECH
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