基于伴随方程和自动微分的雷达散射截面表面灵敏度计算

doi:10.7527/S1000-6893.2023.28508

Abstract

Abstract:

The development of anti-stealth technology poses a strong challenge to the stealth performance of military aircraft. The classical electromagnetic adjoint approach cannot obtain the surface sensitivity information， and therefore cannot provide intuitive guidance for the optimal design. Moreover， in the classical adjoint approach， the need to repeatedly fill the impedance matrix during gradient calculation leads to low efficiency， particularly with a large number of design variables and incident angles. This paper proposes an approach to calculating radar cross section surface sensitivity based on the adjoint and automatic differentiation method. A sparse matrix storage method considering the characters of the Rao-Wilton-Glisson basis function is adopted to reduce the memory requirements of the impedance matrix differentiation. The proposed method can obtain the surface sensitivity of all surface nodes with one matrix differentiation calculation. For different incident angles， the calculation of design variable gradients of any number will not exceed 16 matrix-vector products. The proposed surface sensitivity calculation approach can effectively improve the efficiency of gradient calculation， and provides support to the optimal design of stealth military aircraft.

Key words: electromagnetic adjoint equation, surface sensitivity, radar cross section, automatic differentiation

CLC Number:

V211.3

Lin ZHOU, Xian CHEN, Jiangtao HUANG, Jun DENG, Zhenghong GAO. Radar cross section surface sensitivity calculation based on adjoint approach and automatic differentiation[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(22): 128508-128508.

Figures/Tables 15

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Table 1

Gradient calculation amount comparison of design variables

参数	经典伴随方法	表面灵敏度方法
$∂ Z / ∂ r$ 计算次数	0	1
表面网格变形次数	N	N
矩阵填充次数	N	0
矩阵-矢量乘次数	N $×$ N_a	8N_a
矢量-矢量乘次数	N $×$ N_a	8N_a

Table 1

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Radar cross section surface sensitivity calculation based on adjoint approach and automatic differentiation

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