多約束稀布矩形平面陣列天線的方向圖綜合

戴定成; 姚敏立; 賈維敏; 金偉; 張峰干

doi:10.11999/JEIT180262

多約束稀布矩形平面陣列天線的方向圖綜合

doi: 10.11999/JEIT180262 cstr: 32379.14.JEIT180262

1.
火箭軍工程大學作戰(zhàn)保障學院 ??西安 ??710025
2.
火箭軍工程大學核工程學院 ??西安 ??710025

基金項目: 國家自然科學基金(61179004, 61179005)

詳細信息

作者簡介:
戴定成：男，1991年生，博士生，研究方向為陣列天線的優(yōu)化設(shè)計、陣列信號處理

姚敏立：男，1966年生，教授，博士生導師，主要研究方向為寬帶移動衛(wèi)星通信、陣列信號處理

賈維敏：女，1971年生，教授，博士生導師，主要研究方向為陣列信號處理、寬帶移動衛(wèi)星通信等

金偉：男，1984年生，博士，講師，主要研究方向為陣列信號處理、雷達數(shù)據(jù)處理

張峰干：男，1985年生，博士，講師，主要研究方向為陣列信號處理、陣列天線優(yōu)化

通訊作者:
戴定成　ddc264@163.com

中圖分類號: TN821
計量
- 文章訪問數(shù): 1871
- HTML全文瀏覽量: 695
- PDF下載量: 76
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2018-03-21
- 修回日期: 2018-09-14
- 網(wǎng)絡(luò)出版日期: 2018-10-08
- 刊出日期: 2019-01-01

Synthesis of Multi-constrained Sparse Rectangular Arrays

1.
School of Military Operation Support, Rocket Force University of Engineering, Xi’an 710025, China
2.
School of Nuclear Engineering, Rocket Force University of Engineering, Xi’an 710025, China

Funds: The National Natural Science Foundation of China (61179004, 61179005)

摘要

摘要:
針對多約束稀布矩形陣列天線的優(yōu)化設(shè)計問題，該文提出一種新的矩陣映射(NMM)方法。首先，綜合考慮陣元的可分布范圍與可分布數(shù)量，重新定義陣元坐標矩陣的維數(shù)以提高陣元分布的自由度。其次，當坐標矩陣定義的陣元數(shù)量大于實際陣元數(shù)量時，建立選擇矩陣以確定各陣元的取舍。再次，針對現(xiàn)有矩陣映射方法無法完全避免不可行解的問題，構(gòu)建了一種NMM方法，通過兩種不同的矩陣映射函數(shù)將多約束優(yōu)化問題轉(zhuǎn)換為無約束優(yōu)化問題。最后進行仿真對比實驗，實驗結(jié)果證明了算法的有效性。
- 陣列天線 /
- 稀布平面陣列 /
- 約束優(yōu)化 /
- 旁瓣電平
Abstract:
A Novel Matrix Mapping (NMM) method is proposed for the synthesis of sparse rectangular arrays with multiple constraints. Firstly, the sizes of element coordinate matrices are resized to improve the Degree Of Freedom (DOF) of elements by taking account of both placeable number and distributable range of elements. Then, a selection matrix is established to determine which elements should be turned off when the coordinate matrices should be thinned. By establishing two different mapping functions, a NMM method is presented to overcome the drawbacks of existing methods in terms of flexibility and effectiveness. Finally, comparison experiments are conducted to verify the effectiveness of the proposed method. The numerical validation points out that the proposed method outperforms the existing methods in the design of sparse rectangular arrays.
- Antenna arrays /
- Sparse planar arrays /
- Constrained optimization /
- Sidelobe level

HTML全文

圖 1 稀布矩形平面陣列天線結(jié)構(gòu)圖

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圖 2 矩陣映射法坐標矩陣維數(shù)示意圖

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圖 3 矩陣映射法陣元位置示意圖

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圖 4 矩陣映射法陣元位置示意圖

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圖 5 T₂映射原理

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圖 6 實驗1中A類仿真結(jié)果

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圖 7 實驗1 B類仿真結(jié)果

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圖 8 實驗1 A類實驗蒙特卡洛仿真迭代曲線對比

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圖 9 實驗1 B類實驗蒙特卡洛仿真迭代曲線對比

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圖 10 實驗2仿真結(jié)果

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表 1 實驗1仿真結(jié)果對比(dB)

實驗類型	方法	最優(yōu)值	最差值	均值	方差
A	NMM	–61.2178	–52.3630	–57.8363	4.0813
A	MMM	–53.5222	–50.4478	–51.9317	0.4940
B	NMM	–22.7591	–20.4355	–21.4060	0.1993
B	MMM	–19.1338	–17.9751	–18.5972	0.0875

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表 2 算法運算效率對比

實驗1	方法	平均運行時間(s)	平均內(nèi)存峰值使用量(kB)	平均適應值(dB)	可行解占比(%)
A	MMM算法	247.614	620	–51.9317	44
A	本文方法	283.704	620	–57.8363	100
B	MMM算法	20472.744	904	–18.5972	60
B	本文方法	25823.421	984	–21.4060	100

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表 3 實驗2仿真結(jié)果對比(dB)

實驗類型	方法	最優(yōu)值	最差值	均值	方差
A	NMM	–60.2701	–50.6686	–56.0144	4.7592
B	NMM	–22.0422	–20.1613	–20.9181	0.2303

下載: 導出CSV

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