Linear sparse arrays designed by dynamic constrained multi-objective evolutionary algorithm

The design of linear sparse array is a constrained multi-objective optimization problem(CMOP). There are three objectives: minimization of peak sidelobe level(PSLL), half-power beam width(HPBW) and spatial aperture. The amplitude coefficients of elements and sensor positions of the array are decisio...

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Bibliographic Details
Published in:2014 IEEE Congress on Evolutionary Computation (CEC) pp. 3067 - 3072
Main Authors: Wei Dong, Sanyou Zeng, Yong Wu, Dayue Guo, Lunan Qiao, Zhiqun Liu
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2014
Subjects:
Antenna arrays
Apertures
Dynamic constrained multi-objective evolutionary algorithm(DCMOEA)
Evolutionary computation
half-power beam width(HPBW)
Heuristic algorithms
linear sparse arrays
Optimization
peak sidelobe level(PSLL)
Sociology
spatial aperture
Statistics
ISSN:1089-778X
Online Access:Get full text
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Summary:The design of linear sparse array is a constrained multi-objective optimization problem(CMOP). There are three objectives: minimization of peak sidelobe level(PSLL), half-power beam width(HPBW) and spatial aperture. The amplitude coefficients of elements and sensor positions of the array are decision variables. Dynamic constrained multi-objective evolutionary algorithm(DCMOEA) is used to design linear sparse arrays in this paper. It makes a difference that the output is a set of Pareto solutions (antenna arrays), not just only one solution. The users can choose an array from the set to meet their preferences for low PSLL, small HPBW, small spatial aperture or a trade-off among them. Experimental results showed that the DCMOEA performs better than peer state-of-art algorithms referred in this paper, especially on the arrays' spatial aperture optimization.
ISSN:1089-778X
DOI:10.1109/CEC.2014.6900448