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Sensors 2014, 14(8), 15113-15141; doi:10.3390/s140815113

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

1
Launching Center of Barreira do Inferno, Brazilian Air Force, RN-063 59140-970, Parnamirim RN, Brazil
2
Department of Electrical Engineering, Federal University of Rio Grande do Norte, Campus Universitário 59078-900, Natal RN, Brazil
3
Department of Communications Engineering, Federal University of Rio Grande do Norte, Campus Universitário 59078-900, Natal RN, Brazil
*
Author to whom correspondence should be addressed.
Received: 23 January 2014 / Revised: 20 June 2014 / Accepted: 9 July 2014 / Published: 18 August 2014
(This article belongs to the Section Remote Sensors)
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Abstract

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. View Full-Text
Keywords: rocket tracking systems; phased array radars; radiation pattern control; genetic algorithm; maximum-minimum crossover rocket tracking systems; phased array radars; radiation pattern control; genetic algorithm; maximum-minimum crossover
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Silva, L.W.T.; Barros, V.F.; Silva, S.G. Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems. Sensors 2014, 14, 15113-15141.

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