Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution
A two-stage design approach is proposed to address the sparse antenna array design for multiple-input multiple-output radar. In the first stage, the cyclic algorithm (CA) is used to establish a covariance matrix that satisfies the beam pattern approximation for a full array. In the second stage, a s...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2016/1747843 |
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| _version_ | 1832547228354347008 |
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| author | Zhi-Kun Chen Feng-Gang Yan Xiao-Lin Qiao Yi-Nan Zhao |
| author_facet | Zhi-Kun Chen Feng-Gang Yan Xiao-Lin Qiao Yi-Nan Zhao |
| author_sort | Zhi-Kun Chen |
| collection | DOAJ |
| description | A two-stage design approach is proposed to address the sparse antenna array design for multiple-input multiple-output radar. In the first stage, the cyclic algorithm (CA) is used to establish a covariance matrix that satisfies the beam pattern approximation for a full array. In the second stage, a sparse antenna array with a beam pattern is designed to approximate the desired beam pattern. This paper focuses on the second stage. The optimization problem for the sparse antenna array design aimed at beam pattern synthesis is formulated, where the peak side lobe (PSL) is weakly constrained by the mean squared error. To solve this optimization problem, the differential evolution (DE) algorithm with multistrategy is introduced and PSL suppression is treated as an inequality constraint. However, in doing so, a new multiobjective optimization problem is created. To address this new problem, a multiobjective differential evolution algorithm based on Pareto technique is proposed. Numerical examples are provided to demonstrate the advantages of the proposed approach over state-of-the-art methods, including DE and genetic algorithm. |
| format | Article |
| id | doaj-art-6d4e5447ca35488fb5041a7c6bbd950c |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-6d4e5447ca35488fb5041a7c6bbd950c2025-02-03T06:45:29ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772016-01-01201610.1155/2016/17478431747843Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential EvolutionZhi-Kun Chen0Feng-Gang Yan1Xiao-Lin Qiao2Yi-Nan Zhao3School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin Heilongjiang 150001, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin Heilongjiang 150001, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin Heilongjiang 150001, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin Heilongjiang 150001, ChinaA two-stage design approach is proposed to address the sparse antenna array design for multiple-input multiple-output radar. In the first stage, the cyclic algorithm (CA) is used to establish a covariance matrix that satisfies the beam pattern approximation for a full array. In the second stage, a sparse antenna array with a beam pattern is designed to approximate the desired beam pattern. This paper focuses on the second stage. The optimization problem for the sparse antenna array design aimed at beam pattern synthesis is formulated, where the peak side lobe (PSL) is weakly constrained by the mean squared error. To solve this optimization problem, the differential evolution (DE) algorithm with multistrategy is introduced and PSL suppression is treated as an inequality constraint. However, in doing so, a new multiobjective optimization problem is created. To address this new problem, a multiobjective differential evolution algorithm based on Pareto technique is proposed. Numerical examples are provided to demonstrate the advantages of the proposed approach over state-of-the-art methods, including DE and genetic algorithm.http://dx.doi.org/10.1155/2016/1747843 |
| spellingShingle | Zhi-Kun Chen Feng-Gang Yan Xiao-Lin Qiao Yi-Nan Zhao Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution International Journal of Antennas and Propagation |
| title | Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution |
| title_full | Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution |
| title_fullStr | Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution |
| title_full_unstemmed | Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution |
| title_short | Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution |
| title_sort | sparse antenna array design for mimo radar using multiobjective differential evolution |
| url | http://dx.doi.org/10.1155/2016/1747843 |
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