## Abstract

This work combines two intelligent computing methodologies called Genetic Algorithms and Adaptive Filtering for the comprehensive optimization of an n-element linear antenna array. For the physical design, a Genetic Algorithm is used aimed to reduce the number of elements in the array and to find the distance and the progressive phase change between them, so that they achieve a desired half-power beamwidth (HPBW). These parameters are used to simulate a uniform linear antenna array, to which an adaptive filtering stage was added to maximize a lobe of the radiation pattern at the incident angle of a desired signal and eliminate interference in other directions. The LMS, NLMS, and RLS adaptive filtering algorithms were used and compared to determine which of them provides the best results. The results obtained in the simulations demonstrated that this type of antenna array can be optimized by reducing the number of elements and increasing its efficiency with adaptive algorithms that could be adapted to these arrangements with few elements. Therefore, this technique can be used to optimize any uniform linear array that uses real antennas, other than isotropic, in order to reduce its production costs in the construction of the array and increase its efficiency in its use.

Original language | English |
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Title of host publication | 2020 IEEE ANDESCON, ANDESCON 2020 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

ISBN (Electronic) | 9781728193656 |

DOIs | |

State | Published - 13 Oct 2020 |

Event | 2020 IEEE ANDESCON, ANDESCON 2020 - Quito, Ecuador Duration: 13 Oct 2020 → 16 Oct 2020 |

### Publication series

Name | 2020 IEEE ANDESCON, ANDESCON 2020 |
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### Conference

Conference | 2020 IEEE ANDESCON, ANDESCON 2020 |
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Country/Territory | Ecuador |

City | Quito |

Period | 13/10/20 → 16/10/20 |

### Bibliographical note

Publisher Copyright:© 2020 IEEE.

## Keywords

- Adaptive Filtering
- Antenna
- Antennas Smart Array
- Beamforming
- Computing Intelligence
- Genetic Algorithm
- LMS
- NLMS
- Optimization
- RLS