Hybrid Genetic Algorithm and Modified Iterative Fourier Transform Algorithm for Large Thinned Array Synthesis

Cui, Can; Li, Wen Tao; Ye, Xiu Tiao; Shi, Xiao Wei

doi:10.1109/lawp.2017.2700865

Cited by 33 publications

(13 citation statements)

References 17 publications

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“…The main‐lobe width is 0.0101 (0.577°). Compared with the other algorithms, shown in Table 1, the PSLL obtained by the proposed method is 1.99 dB lower than −22.09 dB by GA [1], 1.16 dB lower than −22.92 dB by IFT [7], 1.03 lower than −23.05 dB by ADSGA [12] and 0.61 dB lower than −23.47 dB by HGAMIFT [9]. What is more, the main‐lobe width obtained by the proposed method is about the same as the other methods or even more narrow.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…In the cases of different sizes and fill factors, the proposed method performs much better solutions compared with GA, IFT, ADSGA and HGAMIFT. It can be concluded that the proposed method gets results ∼3–4 dB lower than that by GA [1], ∼2–3 dB lower than that by IFT [7] and ADSGA [12], ∼0.3–1 dB lower than that by HGAMIFT [9].…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…However, IFT heavily relies on the initialisation and is easy to be trapped in local optimum. Thus, modified and hybrid methods seem to be better solutions [8–10]. In [9], the proposed hybrid genetic algorithm and modified iterative Fourier transform algorithm (HGAMIFT) merges modified IFT technique (MIFT) into the selection step of GA and can get lower results with less time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid optimisation method of improved genetic algorithm and IFT for linear thinned array

Wang

Sun

Yang

et al. 2019

J. eng.

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Section: Numerical Simulationsmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid optimisation method of improved genetic algorithm and IFT for linear thinned array

Wang

Sun

Yang

et al. 2019

J. eng.

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“…As an important research direction of antenna arrays, the theory of array synthesis has attracted more and more attention because it can be applied to customizing the patterns and impedance matching. Therefore, many optimization methods based on analytical methods [1]- [6], stochastic algorithms [7]- [10], convex optimization [11]- [13] and hybrid methods [14]- [17] have been proposed for array synthesis problems in recent years. The above methods have shown excellent performances in many array synthesis problems; however, they all consider the elements in the arrays to be ideal models and the mutual coupling effects among the elements are ignored, which could cause two problems.…”

Section: Introductionmentioning

confidence: 99%

An ANN-Based Synthesis Method for Nonuniform Linear Arrays Including Mutual Coupling Effects

2020

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This paper proposes an artificial neural network (ANN)-based synthesis method for nonuniform linear arrays with mutual coupling effects. The proposed method can simultaneously optimize the location distributions and excitations of the elements. As is well known, for nonuniform linear arrays, the mutual coupling effects on the active element patterns (AEPs) and passive S parameters vary with the location distribution of the elements. However, few papers have focused on how to describe the relationship between the effects of mutual coupling and the location distribution of the elements. In this paper, we use several parallel and independent ANNs to characterize the effects of mutual coupling on the AEPs and passive S parameters in nonuniform linear arrays with variable location distributions. At the same time, to relieve the curse of dimensionality, we make use of the idea of subarrays, which makes it possible for the proposed method to model nonuniform linear arrays with a large number of elements. After building accurate ANN models, we make use of the adaptive differential evolution algorithm JADE to search for the optimal location distribution and the excitations of the elements, to satisfy the requirements for the radiation pattern and the active S parameters of the antenna array. In view of the fact that the mutual coupling effects are accounted for in the synthesis process, the proposed method probably exhibits more practical value in antenna array designs than in other synthesis methods. The validity and efficiency of the proposed method are confirmed based on three examples. INDEX TERMS Artificial neural networks, Mutual coupling effects, Nonuniform linear antenna arrays, Subarray.

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“…Some of the most notable techniques include genetic algorithm (GA) [16], [17], iterative Fourier transform (IFT) [10], [18], particle swarm (PS) [19]- [21], multi-level branch-and-bound (B&B) [22] matrix pencil method [23], Compressive sensing [24], [25], invasive weed (IW) [26], ant colony [27] and so on. A number of hybrid schemes of multiple approaches were also proposed [28], [29]. It is noteworthy that although these approaches show effectiveness in reproducing the desired/reference patterns, some do not work for asymmetrical beams.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing Multiplicative Antenna Array

Abbasi

Fusco

Zelenchuk

2018

IEEE Trans. Antennas Propagat.

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Abstract-This paper presents a novel approach to formulate an aggressively thinned sparse antenna array suitable for orthogonal multi-beam receiver applications. The power patterns of M×N element planar rectangular array are first reduced to orthogonally placed cross multiplicative sub-arrays. These arrays are then re-distributed using a compressive sensing (CS) approach in order to achieve array thinning along two 1-D sub-arrays for a fixed steered beam projection. A multi-beam synthesis approach is then implemented which permits efficient beam maxima as well as null placement of multiple interlaced far-field patterns. Numerical examples are presented to show the implementation of the proposed approach.

show abstract

Hybrid Genetic Algorithm and Modified Iterative Fourier Transform Algorithm for Large Thinned Array Synthesis

Cited by 33 publications

References 17 publications

Hybrid optimisation method of improved genetic algorithm and IFT for linear thinned array

Hybrid optimisation method of improved genetic algorithm and IFT for linear thinned array

An ANN-Based Synthesis Method for Nonuniform Linear Arrays Including Mutual Coupling Effects

Compressive Sensing Multiplicative Antenna Array

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