Design of sparse antenna array with low sidelobe based on deep learning

doi:10.7523/j.ucas.2025.030

Abstract

Abstract:

To meet the requirements of low-cost and low-sidelobe level for space-borne phased array antennas， an optimization method for sparse arrays based on deep learning is proposed. For the optimization problem of large-scale sparse arrays， traditional genetic algorithms face issues such as high computational complexity and time-consuming fitness evaluation during the optimization process. In this paper， a deep-learning model is introduced to predict the sidelobe level， replacing the time-consuming simulation calculations， and significantly reducing the computational complexity. Experimental results show that， compared with traditional genetic algorithms， the method proposed in this paper shows some improvement in optimization effect， exhibits superior computational efficiency particularly suited for high-frequency engineering applications and can effectively solve the optimization problem of large-scale sparse arrays.

Key words: sparse array, deep learning, genetic algorithm, sidelobe optimization

CLC Number:

TN820.1

Yuanming XIAO, Liangxing HE. Design of sparse antenna array with low sidelobe based on deep learning[J]. Journal of University of Chinese Academy of Sciences, 2026, 43(5): 650-656.

Figures/Tables 11

Fig.1 Diagram of sparse array arrangement

Table 1 Design variable compression analysis

40×40

50×50

400

625

100

169

6.25

6.76

Fig.2 The improved genetic algorithm framework in this paper

Table 2 Simulation parameters

仿真参数

数值

频率/GHz

波长/m

阵元间距d/m

满阵的天线单元数N

保留的天线单元数N_R

优化子阵数N_opt

种群规模NP

迭代进化次数G

交叉概率P_c

变异概率P_m

3.55

0.0845

0.044

2500

1404

169

200

100

0.8

0.05

Fig.3 FCN network architecture

Fig.4 Simulation and prediction results for 100 random array layouts in the test set

Fig.5 Schematic diagram of the optimized sparse array arrangement

Fig.6 Array three-dimensional pattern

Fig.7 Array plane pattern

Fig.8 Convergence curve

Table 3 Full array and comparison of two methods

满阵

传统GA

本文

2500

1404

39.92

37.96

38.03

-13.47

-20.94

-22.18

—

267.36

16.27

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