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

Liang, Shuang; Fang, Zhiyi; Sun, Geng; Liu, Yanheng; Qu, Guannan; Zhang, Ying

doi:10.1109/access.2020.2976127

Cited by 61 publications

(33 citation statements)

References 50 publications

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“…Though it outperforms CSO [44], it does not converge with few iterations, and it is computationally time-consuming than PSO. Improved cat swarm optimization (ICSO) was proposed by Liang et al, [1] for the SLL reduction of linear, circular and random antenna array and the result was benchmarked with seven existing heuristic algorithms. It was able to achieve a better optimization performance due to the enhanced local search factor, weighting factor, and global search factor introduced into its update method.…”

Section: Related Workmentioning

confidence: 99%

“…For ages, antennas have been one of the major indispensable entities in the field of wireless communication; been widely used in radar systems, signal processing, telecommunication, and lots more. In this era of building a smart world, smart antennas also known as adaptive array antennas have been a trending focus of most technologically-based sectors due to their advantage of providing high gains and spectral efficiency [1]. It also has some distinctive features of adaptive beamforming and beams steering capability [2][3], hence, extensively employed in the fifth generation (5G) and satellite communication systems.…”

Section: Introductionmentioning

confidence: 99%

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Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm

et al. 2021

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Pattern synthesis is a significant research focus in smart antennas due to its extensive use in several radar and communication systems. To improve the optimization performance of pattern synthesis of uniform and sparse linear antenna array, this paper presents an optimization method for solving the antenna array synthesis problem using the Mayfly Algorithm (MA). MA is a new heuristic algorithm inspired by the flight behavior as well as the mating process of mayflies, it has a unique velocity update system with great convergence. In this work, the MA was applied to linear antenna arrays (LAA) for optimal pattern synthesis in the following ways: by optimizing the antenna current amplitudes while maintaining uniform spacing and by optimizing the antenna positions while assuming a uniform excitation. Constraints of inter-element spacing and aperture length are imposed in the synthesis of sparse LAA. Sidelobe level (SLL) suppression with the placement of nulls in the specified directions is also implemented. The results gotten from this novel algorithm are validated by benchmarking with results obtained using other intelligent algorithms. In the synthesis of uniform 20-element LAA with nulls, MA achieved an SLL of -31.27 dB and the deepest null of -101.60 dB. Also, for sparse 20-element LAA, an SLL of -18.85 dB was attained alongside the deepest null of -87.37 dB. MA obtained an SLL of -35.73 dB and -23.68 dB for the synthesis of uniform and sparse 32-element LAA respectively. Finally, electromagnetism simulations are conducted using ANSYS Electromagnetics (HFSS) software, to evaluate the performance of MA for the beam pattern optimizations, taking into consideration the mutual coupling effects. The results prove that optimization of LAA using MA provides considerable enhancements in peak SLL suppression, null control, and convergence rate with respect to the uniform array and the synthesis obtained from other existing optimization techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm

et al. 2021

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“…Pattern synthesis of LAA is realized by using improved differential evolution algorithm in [13]. Three kinds of antenna arrays such as LAA, circular antenna array (CAA) and random antenna array (RAA) are studied in [14] with Chicken Swarm Optimization (CSO). The strawberry algorithm (SBA) as an optimization tool has been applied to antenna array synthesis and faster convergence characteristic is obtained in addition to improvement in side-lobe suppression and prescribed null placements [15].…”

Section: Introductionmentioning

confidence: 99%

A comparison of swarm-based optimization algorithms in linear antenna array synthesis

2021

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“…The fly in the ointment is that no such kind of algorithm can perfectly solve all optimization problems. Although the conventional CSO provides a commendable idea, each solution update method is not effective, which results in a decrease in the overall search ability of the algorithm [39], [40]. Hence, these circumstances above prompt us to propose an improved version of the conventional CSO algorithm for solving the SVM robust beamforming optimization model.…”

Section: Introductionmentioning

confidence: 99%

Robust Array Beamforming via an Improved Chicken Swarm Optimization Approach

2021

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Robust array beamforming is a challenging task in radar, sonar and communications due to the influence of direction of arrival (DOA) mismatch and sensor position errors. However, how to enhance the robustness of beamforming is a key issue in antenna arrays. The current paper focuses on a novel approach called the improved chicken swarm optimization (ICSO) method to settle the optimization model of conventional linearly constrained minimum variance (LCMV) based on support vector machine (SVM) to against the mismatch problems as well as control the sidelobe level (SLL). As far as the ICSO method is concerned, considering that the particle swarm optimization (PSO) algorithm has outstanding convergence performance in the early iteration, the dominance of the alpha wolf in the grey wolf optimization (GWO) algorithm and the innovative mutual attraction mechanism in the firefly algorithm (FA), and we introduce these three strategies into the solution update method of conventional chicken swarm optimization (CSO) algorithm for achieving better optimization capability. Moreover, an operation of removing duplicate solutions is proposed to enhance the utilization of the population. In terms of the SVM-based LCMV beamforming algorithm, we adopt the so-called linear ε -insensitive loss function to reconstruct the final cost function of LCMV by penalizing the errors between the actual and ideal array responses. Finally, we conduct simulations to evaluate the performance of the swarm intelligent optimization algorithms under an ideal scenario without mismatch and an actual scenario with the mismatch, respectively. And the results demonstrate that the developed ICSO algorithm obtains excellent robustness for different scenarios compared to PSO, FA, GWO and CSO optimization algorithms.INDEX TERMS Robust array beamforming, improved chicken swarm optimization, linearly constrained minimum variance, support vector machine, sidelobe level, steering vector errors.

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

Cited by 61 publications

References 50 publications

Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm

Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm

A comparison of swarm-based optimization algorithms in linear antenna array synthesis

Robust Array Beamforming via an Improved Chicken Swarm Optimization Approach

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