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Large thinned array design based on multi-objective cross entropy algorithm

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Abstract

To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy (CE) algorithm is proposed by combining fuzzy c-mean clustering algorithm with traditional cross entropy algorithm, and specific program flow of the algorithm is given. Using the algorithm, large thinned array (200 elements) given sidelobe level (−10, −19 and −30 dB) problem is solved successfully. Compared with the traditional statistical algorithms, the optimization results of the algorithm validate that the number of feed array elements reduces by 51%, 11% and 6% respectively. In addition, compared with the particle swarm optimization (PSO) algorithm, the number of feed array elements from the algorithm is more similar, but the algorithm is more efficient.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Heilongjiang University of Science and Technology, Harbin, 150022, China

    Li Bian  (边 莉) & Chen-yuan Bian  (边晨源)

  2. Department of Electrical and Computer Engineering, Auburn University, Auburn, 36849, USA

    Shu-min Wang  (王书民)

Authors
  1. Li Bian  (边 莉)
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  2. Chen-yuan Bian  (边晨源)
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  3. Shu-min Wang  (王书民)
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Corresponding author

Correspondence to Li Bian  (边 莉).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 51474100), the Youth Science Fund of Heilongjiang Province in China (No. QC2010023), and the Youth Outstanding Ability Program in Heilongjiang University of Science and Technology

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Bian, L., Bian, Cy. & Wang, Sm. Large thinned array design based on multi-objective cross entropy algorithm. J. Shanghai Jiaotong Univ. (Sci.) 20, 437–442 (2015). https://doi.org/10.1007/s12204-015-1645-4

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  • DOI: https://doi.org/10.1007/s12204-015-1645-4

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