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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Bibliographic Details
Published in:International journal of antennas and propagation 2016-01, Vol.2016 (2016), p.1-12
Main Authors: Zhao, Yi-nan, Qiao, Xiao-Lin, Yan, Fenggang, Chen, Zhi-Kun
Format: Article
Language:English
Subjects:
Algorithms
Antenna arrays
Antennas
Beams (radiation)
Design
Evolutionary algorithms
Multiple objective analysis
Mutation
Optimization
Propagation
Radar
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Summary: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.
ISSN:1687-5869
1687-5877
DOI:10.1155/2016/1747843