Dynamic Thinning of Antenna Array Using Genetic Algorithm

Jain, Rajashree; Mani, Ganapathy

doi:10.2528/pierb11042203

Cited by 24 publications

(14 citation statements)

References 27 publications

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“…GA is mainly composed of three operators: reproduction, crossover and mutation [9][10][11][12][13][14][15][16]. It is operated in the following steps: 1) create an initial population; 2) evaluate the fitness of each population member; 3) invoke natural selection; 4) select population members for mating; 5) generate offspring; 6) mutate selected members of the population; 7) terminate run or go to step 2).…”

Section: Calculation Modelmentioning

confidence: 99%

Application of the Cosine Window Function With the Parameters Optimized by Genetic Algorithm in Bistatic Planar Near-Field Scattering Measurements

Ding¹,

Zhai²,

Xie³

et al. 2012

PIER Letters

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Abstract-The cosine window function with the parameters optimized by Genetic Algorithm (GA) is applied in bistatic planar nearfield scattering measurements so as to effectively improve the measurement precision. With the infinitely long ideal conductor cylinder as the target under test, the bistatic planar near-field scattering measurement technique is studied by the method of computer simulation and some useful results and basic laws are obtained. The calculation results show that the truncation errors caused by finite scan plane in the far-field Radar Cross Section (RCS) of the target under test obtained by near-field to far-field transformation can be reduced greatly by the weighting process of the measured scattered near-field data by means of the cosine window function with the parameters optimized by GA.

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Section: Calculation Modelmentioning

confidence: 99%

Application of the Cosine Window Function With the Parameters Optimized by Genetic Algorithm in Bistatic Planar Near-Field Scattering Measurements

Ding¹,

Zhai²,

Xie³

et al. 2012

PIER Letters

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“…Such a prior knowledge can help in partitioning of the array into zones and better exploitation of the solution space. In general, zoning is based on dividend or return likely to yield while exploiting the zone [18,19]. Though it is possible to partition into any number of zones, it may not be advisable to consider more than 2 or 3 zones, as shown later.…”

Section: Zoning Techniquementioning

confidence: 99%

Solving “Antenna Array Thinning Problem” Using Genetic Algorithm

Jain

Mani²

2012

Applied Computational Intelligence and Soft Computing

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Thinning involves reducing total number of active elements in an antenna array without causing major degradation in system performance. Dynamic thinning is the process of achieving this under real-time conditions. It is required to find a strategic subset of antenna elements for thinning so as to have its optimum performance. From a mathematical perspective this is a nonlinear, multidimensional problem with multiple objectives and many constraints. Solution for such problem cannot be obtained by classical analytical techniques. It will be required to employ some type of search algorithm which can lead to a practical solution in an optimal. The present paper discusses an approach of using genetic algorithm for array thinning. After discussing the basic concept involving antenna array, array thinning, dynamic thinning, and application methodology, simulation results of applying the technique to linear and planar arrays are presented.

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“…The GA has been successfully applied in the optimization of various antennas [13][14][15][16][17]. In this work, it is employed for optimizing the proposed microstrip grid-array antenna.…”

Section: Antenna Optimizationmentioning

confidence: 99%

A Novel Microstrip Grid Array Antenna With Both High-Gain and Wideband Properties

Pan¹,

Xing²,

Ren³

et al. 2013

PIER C

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Abstract-A novel microstrip grid array antenna that is simultaneously high in gain and wide in bandwidth is proposed. To enhance its bandwidth, the antenna adopts elliptically shaped and variably dimensioned radiation elements as well as a linearly tapered ground plane, and is optimized by a parallel genetic algorithm (GA) on a cluster system. A prototype antenna was fabricated and tested. Results of simulation and measurement agree well and show the antenna exhibits encouraging properties, e.g., a maximum gain of approximately 15.1 dBi at 5.8 GHz; the |S 11 | < −10 dB bandwidth and the 3 dB gaindrop bandwidth are 25.6% (from 5.03 GHz to 6.51 GHz) and 27.6% (from 5.0 GHz to 6.6 GHz), respectively, of the center frequency, both of which are much wider than that of conventional microstrip grid array antennas. Moreover, the overlap between the antenna's impedance bandwidth and the gain bandwidth results in a wide effective operating frequency bandwidth of 25.6%, which is the largest so far achieved for microstrip grid-array antennas.

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Dynamic Thinning of Antenna Array Using Genetic Algorithm

Cited by 24 publications

References 27 publications

Application of the Cosine Window Function With the Parameters Optimized by Genetic Algorithm in Bistatic Planar Near-Field Scattering Measurements

Application of the Cosine Window Function With the Parameters Optimized by Genetic Algorithm in Bistatic Planar Near-Field Scattering Measurements

Solving “Antenna Array Thinning Problem” Using Genetic Algorithm

A Novel Microstrip Grid Array Antenna With Both High-Gain and Wideband Properties

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