Sparse circular array pattern optimization based on MOPSO and convex optimization

Cao, Aihua; Li, Hailin; Ma, Shoulei; Tan, Jianrong; Zhou, Jianjiang

doi:10.1109/apmc.2015.7412993

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…The algorithms involved in the simulation include the proposed DPSO-HSS algorithm, RDPSO algorithm in [ 13 ], NPSOWM algorithm in [ 14 ], and MOPSO-CO algorithm in [ 4 ].…”

Section: Numerical Results and Analysismentioning

confidence: 99%

“…Rahardjo et al [ 3 ] developed a new method for designing linear thinned arrays using spacing coefficients based on Taylor line source distribution, which is verified to be able to correctly match different beam pattern configurations without repeated global optimization. Recently, analytical thinning methods by convex optimization [ 4 ] and Bayesian compressive sensing [ 5 ] have been introduced, but these methods need to set a suitable reference pattern as a precondition. Keizer [ 6 ] proposed the iterative Fourier technique (IFT) for array thinning.…”

Section: Introductionmentioning

confidence: 99%

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Constrained Planar Array Thinning Based on Discrete Particle Swarm Optimization with Hybrid Search Strategies

Cai

Guo

et al. 2022

Sensors

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This article presents a novel optimization algorithm for large array thinning. The algorithm is based on Discrete Particle Swarm Optimization (DPSO) integrated with some different search strategies. It utilizes a global learning strategy to improve the diversity of populations at the early stage of optimization. A dispersive solution set and the gravitational search algorithm are used during particle velocity updating. Then, a local search strategy is enabled in the later stage of optimization. The particle position is adaptively adjusted by the mutation probability, and its motion state is monitored by two observation parameters. The peak side-lobe level (PSLL) performance, effectiveness and robustness of the improved PSO algorithm are verified by several representative examples.

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“…The algorithms involved in the simulation include the proposed DPSO-HSS algorithm, RDPSO algorithm in [ 13 ], NPSOWM algorithm in [ 14 ], and MOPSO-CO algorithm in [ 4 ].…”

Section: Numerical Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constrained Planar Array Thinning Based on Discrete Particle Swarm Optimization with Hybrid Search Strategies

Cai

Guo

et al. 2022

Sensors

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“…Theoretically, the low side lobe level (SLL) can be obtained by optimizing the feeding current and position of each element. Some algorithms are employed to large scale sparse array optimizations, such as genetic algorithm (GA) [3], particle swarm optimization [4], and Harmony Search Algorithm [5]. Among the optimization methods, genetic algorithm is one of the most popular optimization techniques used for side lobe level reduction [6][7][8][9], since GAs [10] are well suited for sparse and thinning array optimization.…”

Section: Introductionmentioning

confidence: 99%

Investigation on SIW Slot Antenna Array with Beam Scanning Ability

2019

International Journal of Antennas and Propagation

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A sparse substrate integrated waveguide (SIW) slot antenna array and its application on phase scanning are studied in this paper. The genetic algorithm is used to optimize the best arrangement for 8-element and 7-element sparse arrays over an aperture of 4.5λ0. Antenna arrays with feeding networks, for steering the main beam pointing to 0° and −15°, are demonstrated with the SIW technology. The comparison between the sparse array and the conventional uniformly spaced array with the same aperture are presented, which suggest that the same beam width can be obtained with the gain decreased by 0.5 or 1 dBi and the number of element reduced by 2 or 3, respectively. The sparse antenna array with beam scanning ability presented in this paper shows that, while the beam scanning in the range of ±15°, the gain fluctuation is less than 0.3 dBi and the side lobe level is lower than −10 dB.

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“…Various optimization methods have been reported to synthesize sparse circular arrays in [14][15][16][17][18][19][20][21][22], including analytical techniques and evolutionary algorithms. In [14] and [15], the vector mapping (VM) method and matrix mapping method were used to handle complicated constraints for linear and planar arrays, respectively.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Constraint Synthesis of Rotationally Symmetric Sparse Circular Arrays Using a Hybrid Algorithm

Wang¹,

Jiao²

2019

PIER M

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Rotationally symmetric sparse circular arrays are synthesized under multiple constraints. By combining the modified differential evolution algorithm based on the harmony search (in short HSDE) with the vector mapping (VM) method, a hybrid algorithm, called VM-HSDE, is proposed for synthesizing sparse circular arrays with low sidelobe levels. Due to the array's specific geometry, the number of optimization variables is reduced, and the constrained optimization problem is simplified. Moreover, infeasible solutions are avoided, and the problem is effectively solved by the VM-HSDE algorithm. Finally, three pattern optimization results verify the effectiveness and reliability of the VM-HSDE algorithm.

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Sparse circular array pattern optimization based on MOPSO and convex optimization

Cited by 6 publications

References 5 publications

Constrained Planar Array Thinning Based on Discrete Particle Swarm Optimization with Hybrid Search Strategies

Constrained Planar Array Thinning Based on Discrete Particle Swarm Optimization with Hybrid Search Strategies

Investigation on SIW Slot Antenna Array with Beam Scanning Ability

Multiple-Constraint Synthesis of Rotationally Symmetric Sparse Circular Arrays Using a Hybrid Algorithm

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