TY - GEN
T1 - Array antenna pattern synthesis method based on intelligent algorithm
AU - He, Zhang
AU - Hua, Zong
AU - Hongmei, Li
AU - Beijia, Liu
AU - Qun, Wu
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/3/15
Y1 - 2017/3/15
N2 - The paper introduced the basic theories of intelligent algorithms, equally spaced linear antenna array and antenna pattern synthesis principle, also introduced the related omni-directional antenna. The procedure, parameter settings, characteristics of genetic algorithm and the neural network algorithms were demonstrated that used in the pattern synthesis, then the simulation programs were given. In the experimental stage, three groups of three different DOA of interference signal were applied to the antenna array model, respectively. The genetic algorithm and neural network algorithm were applied to the array pattern that simulated, then the simulation results were statisticed to compare their accuracy and robustness. At the same time, the linear antenna array model was established by the FEKO simulation software, whose antenna element is omni-directional COCO antenna whose center frequency is 1.8GHz. The weight coefficient produced by genetic algorithm and neural network algorithm were applied to the excitation voltage of every array elements, whose amplitude and phase is controlled by the weight coefficient. Then the results were analyzed which came from the two intelligent algorithms with different interference signals.
AB - The paper introduced the basic theories of intelligent algorithms, equally spaced linear antenna array and antenna pattern synthesis principle, also introduced the related omni-directional antenna. The procedure, parameter settings, characteristics of genetic algorithm and the neural network algorithms were demonstrated that used in the pattern synthesis, then the simulation programs were given. In the experimental stage, three groups of three different DOA of interference signal were applied to the antenna array model, respectively. The genetic algorithm and neural network algorithm were applied to the array pattern that simulated, then the simulation results were statisticed to compare their accuracy and robustness. At the same time, the linear antenna array model was established by the FEKO simulation software, whose antenna element is omni-directional COCO antenna whose center frequency is 1.8GHz. The weight coefficient produced by genetic algorithm and neural network algorithm were applied to the excitation voltage of every array elements, whose amplitude and phase is controlled by the weight coefficient. Then the results were analyzed which came from the two intelligent algorithms with different interference signals.
KW - Array antenna pattern synthesis
KW - Genetic algorithm
KW - Neural network algorithm
UR - https://www.scopus.com/pages/publications/85017228631
U2 - 10.1109/ICEICT.2016.7879764
DO - 10.1109/ICEICT.2016.7879764
M3 - 会议稿件
AN - SCOPUS:85017228631
T3 - Proceedings of 2016 IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2016
SP - 549
EP - 551
BT - Proceedings of 2016 IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2016
Y2 - 20 August 2016 through 22 August 2016
ER -