An efficient synthesis of unequally spaced antenna array with electronic scan capability utilizing particle swarm optimization

Miranda, Allen V.; Ashwin, Peter; Sharan, Preeta; Gangwar, V. S.; Singh, Anil Kumar; Singh, S. P.

doi:10.1109/imarc.2017.8611005

Cited by 4 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PSLLs are −18.32 dB, −18.30 dB, and −18.31 dB, respectively. The difference is less than 0.1 dB, which is consistent with the conclusion in [ 21 ].…”

Section: Numerical Results and Analysissupporting

confidence: 92%

See 1 more Smart Citation

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

Cai

Guo

et al. 2022

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…The PSLLs are −18.32 dB, −18.30 dB, and −18.31 dB, respectively. The difference is less than 0.1 dB, which is consistent with the conclusion in [ 21 ].…”

Section: Numerical Results and Analysissupporting

confidence: 92%

“…In the above example, we set the main beam to a normal direction. Reference [ 21 ] proved that the main beam direction configuration has no impact to PSLL in unequally spaced linear antenna array thinning. In fact, this conclusion could also be obtained for planar array thinning.…”

Section: Numerical Results and Analysismentioning

confidence: 99%

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

Cai

Guo

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The NUSLA was initially developed to overcome the trade-off between the BW and SLL of the narrowband ULA. Several heuristic algorithms, such as simulated annealing (SA) [ 17 ], genetic algorithm (GA) [ 18 ], firefly algorithm (FA) [ 19 ], particle swarm optimization (PSO) [ 20 ], and salp swarm algorithm (SSA) [ 21 ], have been proposed to optimize the weights and spacing of the NUSLA in an analytic form.…”

Section: Introductionmentioning

confidence: 99%

Design Method for a Wideband Non-Uniformly Spaced Linear Array Using the Modified Reinforcement Learning Algorithm

Kang

Kim

Park

et al. 2022

Sensors

View full text Add to dashboard Cite

In this paper, we present a design method for a wideband non-uniformly spaced linear array (NUSLA), with both symmetric and asymmetric geometries, using the modified reinforcement learning algorithm (MORELA). We designed a cost function that provided freedom to the beam pattern by setting limits only on the beam width (BW) and side-lobe level (SLL) in order to satisfy the desired BW and SLL in the wide band. We added the scan angle condition to the cost function to design the scanned beam pattern, as the ability to scan a beam in the desired direction is important in various applications. In order to prevent possible pointing angle errors for asymmetric NUSLA, we employed a penalty function to ensure the peak at the desired direction. Modified reinforcement learning algorithm (MORELA), which is a reinforcement learning-based algorithm used to determine a global optimum of the cost function, is applied to optimize the spacing and weights of the NUSLA by minimizing the proposed cost function. The performance of the proposed scheme was verified by comparing it with that of existing heuristic optimization algorithms via computer simulations.

show abstract

Particle Swarm Optimization (PSO) for Multi-Objective Performance Optimization of Microstrip Patch Antenna for Wide-band Applications

Mishra

Chaurasia

et al. 2022

2022 7th International Conference on Communication and Electronics Systems (ICCES)

View full text Add to dashboard Cite

An efficient synthesis of unequally spaced antenna array with electronic scan capability utilizing particle swarm optimization

Cited by 4 publications

References 15 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

Design Method for a Wideband Non-Uniformly Spaced Linear Array Using the Modified Reinforcement Learning Algorithm

Particle Swarm Optimization (PSO) for Multi-Objective Performance Optimization of Microstrip Patch Antenna for Wide-band Applications

Contact Info

Product

Resources

About