4.6 Article

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

期刊

SENSORS
卷 14, 期 8, 页码 15113-15141

出版社

MDPI
DOI: 10.3390/s140815113

关键词

rocket tracking systems; phased array radars; radiation pattern control; genetic algorithm; maximum-minimum crossover

资金

  1. Brazilian Air Force (FAB, Forca Aerea Brasileira)
  2. Federal University of Rio Grande do Norte (UFRN, the Universidade Federal do Rio Grande do Norte)
  3. Brazilian Federal Agency for the Support and Evaluation of Graduate Education (CAPES, the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior)
  4. Brazilian National Counsel of Technological and Scientific Development (CNPq, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico)

向作者/读者索取更多资源

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.

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