Joint sidelobe suppression and nulls control of large‐scale linear antenna array using particle swarm optimization with global search and population mutation

Zheng, Tingting; Liu, Yanheng; Sun, Geng; Liang, Shuang; Han, Jia‐Wei; Ju, Qianao; Li, Shujing

doi:10.1002/jnm.2710

Cited by 8 publications

(9 citation statements)

References 41 publications

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“…Moreover, these elements are assumed as the isotropic radiators. According to the electromagnetic wave superposition principle, the AF of an LAA can be expressed as follows 36 :

{AF}_{LAA} ((), ϕ) = \sum_{n = - N}^{N} I_{n} italiccos ((), {kx}_{n} italiccosϕ + φ_{n})

where k represents the wave number, I n and x n are the excitation current weight and location of the n th element, respectively, α n is the phase of the n th element and ϕ is the azimuth angle measured from the positive x axis.…”

Section: Af and Fitness Functionmentioning

confidence: 99%

“…These elements are uniformly placed on a ring with the radius of a . The AF of a CAA is as follows 36 :

{AF}_{CAA} ((),, θ, ϕ) = \sum_{n = 1}^{N} I_{n} italicexp ((), j ([], italickasin ((), θ) italiccos ((), ϕ - ϕ_{n}) + α_{n}))

italicka = \frac{2 π}{λ} a = \sum_{i = 1}^{N} d_{i}

ϕ_{n} = \frac{2 π \sum_{i = 1}^{N} d_{i}}{ka}

α_{n} = - italickasin ((), θ_{0}) italiccos ((), ϕ_{0} - ϕ_{n})

where I n is the excitation current weight of the n th element, α n is the phase of the n th element, and d n represents the arc distance between elements n and n − 1. ϕ n and θ n represent the azimuth angle measured from the positive x axis and the elevation angle measured from the z axis, respectively. Moreover, θ 0 and ϕ 0 are set to be 90 ° and 0 ° , respectively.…”

Section: Af and Fitness Functionmentioning

confidence: 99%

“…Moreover, these elements are assumed as the isotropic radiators. According to the electromagnetic wave superposition principle, the AF of an LAA can be expressed as follows 36 :…”

Section: Af and Fitness Functionmentioning

confidence: 99%

“…These elements are uniformly placed on a ring with the radius of a. The AF of a CAA is as follows 36 :…”

Section: Array Factormentioning

confidence: 99%

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An improved cuckoo search with reverse learning and invasive weed operators for suppressing sidelobe level of antenna arrays

Lin

Wang

Zhang

et al. 2020

Int J Numerical Modelling

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In order to overcome some shortcomings including the premature convergence and slow convergence speed at later evolution stage of conventional cuckoo search (CS) algorithm for the sidelobe suppressions of antenna arrays, an improved CS with reverse learning and invasive weed operators (ICSRLIWO) is proposed. First, ICSRLIWO algorithm generates reverse populations through opposition‐based learning method, then selects better individuals in the mixed population consists of the original and reversed population to form a high‐quality initial population. Second, ICSRLIWO introduces the reproducing, space diffusion and survival competition operators of invasive weed optimization to improve the population diversity and global search ability of conventional CS algorithm. Simulations are conducted based on 30 test functions in the CEC 2014 test suit to verify the performance of the proposed approach, and the results show that ICSRLIWO has higher solution accuracy and faster convergence speed than other comparison algorithms. In addition, ICSRLIWO also has advantages in solving the array antenna beam pattern optimization problems.

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“…Moreover, these elements are assumed as the isotropic radiators. According to the electromagnetic wave superposition principle, the AF of an LAA can be expressed as follows 36 :

{AF}_{LAA} ((), ϕ) = \sum_{n = - N}^{N} I_{n} italiccos ((), {kx}_{n} italiccosϕ + φ_{n})

Section: Af and Fitness Functionmentioning

confidence: 99%

“…These elements are uniformly placed on a ring with the radius of a . The AF of a CAA is as follows 36 :

{AF}_{CAA} ((),, θ, ϕ) = \sum_{n = 1}^{N} I_{n} italicexp ((), j ([], italickasin ((), θ) italiccos ((), ϕ - ϕ_{n}) + α_{n}))

italicka = \frac{2 π}{λ} a = \sum_{i = 1}^{N} d_{i}

ϕ_{n} = \frac{2 π \sum_{i = 1}^{N} d_{i}}{ka}

α_{n} = - italickasin ((), θ_{0}) italiccos ((), ϕ_{0} - ϕ_{n})

Section: Af and Fitness Functionmentioning

confidence: 99%

“…Moreover, these elements are assumed as the isotropic radiators. According to the electromagnetic wave superposition principle, the AF of an LAA can be expressed as follows 36 :…”

Section: Af and Fitness Functionmentioning

confidence: 99%

“…These elements are uniformly placed on a ring with the radius of a. The AF of a CAA is as follows 36 :…”

Section: Array Factormentioning

confidence: 99%

See 2 more Smart Citations

An improved cuckoo search with reverse learning and invasive weed operators for suppressing sidelobe level of antenna arrays

Lin

Wang

Zhang

et al. 2020

Int J Numerical Modelling

Self Cite

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show abstract

“…The high sidelobe levels (SLLs) of beam patterns will be occurred if the LAA and CAA are not optimized [7], [21]. For the RAA-based CB transmission, the element nodes are always with random distribution and this may cause higher maximum SLLs of the beam patterns [22], which means the increasing of interferences.…”

Section: Introductionmentioning

confidence: 99%

Sidelobe Reductions of Antenna Arrays via an Improved Chicken Swarm Optimization Approach

et al. 2020

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Antenna arrays are able to improve the directivity performance and reduce the cost of wireless communication systems. However, how to reduce the maximum sidelobe level (SLL) of the beam pattern is a key problem in antenna arrays. In this paper, three kinds of antenna arrays that are linear antenna array (LAA), circular antenna array (CAA) and random antenna array (RAA) are investigated. First, we formulate the SLL suppression optimization problems of LAA, CAA and RAA, respectively. Then, we propose a novel method called improved chicken swarm optimization (ICSO) approach to solve the formulated optimization problems. ICSO introduces four enhanced strategies including the local search factor, weighting factor and global search factor into the update method of conventional chicken swarm optimization (CSO) algorithm, respectively, for achieving better beam pattern optimization results of antenna arrays. Moreover, a variation mechanism is proposed to enhance the population diversity so that further improving the performance of the algorithm. We conduct simulations to evaluate the performance of the proposed ICSO for the maximum SLL suppressions of LAAs, CAAs and RAAs, and the results show that ICSO obtains lower maximum SLLs for different antenna array cases with different numbers of antenna elements compared to several other algorithms.

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An improved biogeography‐based optimization approach for beam pattern optimizations of linear and circular antenna arrays

Liu

Wang

Zheng

et al. 2021

Int J Numerical Modelling

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Antenna array makes an increasingly valuable contribution to various fields of communications. The beam pattern synthesis is one of the significant properties of antenna array. In this article, suppressing the maximum sidelobe levels (SLLs) of different antenna arrays is our main objective. Specifically, we first formulate the beam pattern optimization problems of the linear antenna array (LAA) and circular antenna array (CAA), respectively, and propose an improved biogeography‐based optimization (IBBO) approach to tackle the issue. IBBO introduces the hybrid migration and hybrid mutation operators by adopting several improved factors to strengthen the effect of conventional biogeography‐based optimization (BBO) algorithm. Emulate experiments are built to evaluate the performance of the IBBO for dealing with the formulated antenna array beam pattern optimization problems comprehensively.

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Joint sidelobe suppression and nulls control of large‐scale linear antenna array using particle swarm optimization with global search and population mutation

Cited by 8 publications

References 41 publications

An improved cuckoo search with reverse learning and invasive weed operators for suppressing sidelobe level of antenna arrays

An improved cuckoo search with reverse learning and invasive weed operators for suppressing sidelobe level of antenna arrays

Sidelobe Reductions of Antenna Arrays via an Improved Chicken Swarm Optimization Approach

An improved biogeography‐based optimization approach for beam pattern optimizations of linear and circular antenna arrays

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