Cargando…

Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems

In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic c...

Descripción completa

Detalles Bibliográficos
Autores principales: Silva, Leonardo W. T., Barros, Vitor F., Silva, Sandro G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4179080/
https://www.ncbi.nlm.nih.gov/pubmed/25196013
http://dx.doi.org/10.3390/s140815113
_version_ 1782337016365580288
author Silva, Leonardo W. T.
Barros, Vitor F.
Silva, Sandro G.
author_facet Silva, Leonardo W. T.
Barros, Vitor F.
Silva, Sandro G.
author_sort Silva, Leonardo W. T.
collection PubMed
description In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence.
format Online
Article
Text
id pubmed-4179080
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-41790802014-10-02 Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems Silva, Leonardo W. T. Barros, Vitor F. Silva, Sandro G. Sensors (Basel) Article In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence. MDPI 2014-08-18 /pmc/articles/PMC4179080/ /pubmed/25196013 http://dx.doi.org/10.3390/s140815113 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Silva, Leonardo W. T.
Barros, Vitor F.
Silva, Sandro G.
Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems
title Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems
title_full Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems
title_fullStr Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems
title_full_unstemmed Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems
title_short Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems
title_sort genetic algorithm with maximum-minimum crossover (ga-mmc) applied in optimization of radiation pattern control of phased-array radars for rocket tracking systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4179080/
https://www.ncbi.nlm.nih.gov/pubmed/25196013
http://dx.doi.org/10.3390/s140815113
work_keys_str_mv AT silvaleonardowt geneticalgorithmwithmaximumminimumcrossovergammcappliedinoptimizationofradiationpatterncontrolofphasedarrayradarsforrockettrackingsystems
AT barrosvitorf geneticalgorithmwithmaximumminimumcrossovergammcappliedinoptimizationofradiationpatterncontrolofphasedarrayradarsforrockettrackingsystems
AT silvasandrog geneticalgorithmwithmaximumminimumcrossovergammcappliedinoptimizationofradiationpatterncontrolofphasedarrayradarsforrockettrackingsystems