強(qiáng)稀疏低副瓣近場聚焦稀疏陣列三維成像

楊磊; 宋昊; 申瑞陽; 陳英杰; 胡仲偉; 霍鑫; 邢孟道

doi:10.11999/JEIT231278

強(qiáng)稀疏低副瓣近場聚焦稀疏陣列三維成像

doi: 10.11999/JEIT231278 cstr: 32379.14.JEIT231278

1.
中國民航大學(xué)電子信息與自動化學(xué)院天津 300300
2.
西安電子科技大學(xué)雷達(dá)信號處理國家重點實驗室西安 710071

基金項目: 國家自然科學(xué)基金(62271487)

詳細(xì)信息

作者簡介:
楊磊：男，教授，研究方向為高分辨SAR成像及機(jī)器學(xué)習(xí)理論應(yīng)用

宋昊：男，碩士生，研究方向為毫米波成像與稀疏陣列構(gòu)型設(shè)計

申瑞陽：男，碩士生，研究方向為毫米波成像與稀疏陣列構(gòu)型設(shè)計

陳英杰：男，碩士生，研究方向為毫米波成像與稀疏陣列構(gòu)型設(shè)計

胡仲偉：男，講師，研究方向為高分辨SAR成像及優(yōu)化學(xué)習(xí)理論

霍鑫：男，碩士生，研究方向為毫米波成像與稀疏陣列構(gòu)型設(shè)計

邢孟道：男，教授，研究方向為雷達(dá)成像、動目標(biāo)檢測

通訊作者:
楊磊 email:　yanglei840626@163.com

中圖分類號: TN957
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- 文章訪問數(shù): 243
- HTML全文瀏覽量: 133
- PDF下載量: 45
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2023-11-20
- 修回日期: 2024-11-10
- 網(wǎng)絡(luò)出版日期: 2024-11-19
- 刊出日期: 2024-12-01

High Sparsity and Low Sidelobe Near-field Focused Sparse Array for Three-Dimensional Imagery

1.
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
2.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (62271487)

摘要

摘要: 在主動式電掃描毫米波安檢成像中，均勻陣列天線存在成本受限以及復(fù)雜度高等瓶頸問題，難以在實際工程中大規(guī)模運用。由此，該文提出一種強(qiáng)稀疏低副瓣的近場聚焦稀疏陣列設(shè)計方法，并進(jìn)一步利用改進(jìn)3維時域成像算法實現(xiàn)高精度3維重建。首先，以近場聚焦位置以及峰值旁瓣電平為約束，以權(quán)向量的$ {\ell _p} $(0<p<1)范數(shù)正則化為目標(biāo)函數(shù)，構(gòu)建近場聚焦稀疏陣列天線優(yōu)化模型。然后，通過引入輔助變量，建立旁瓣及聚焦位置約束與輔助變量間的等價代換模型，解決陣列權(quán)向量目標(biāo)函數(shù)與復(fù)雜約束耦合帶來的求解難題，通過等價代換思想對模型化簡并求解。接著，采用復(fù)數(shù)求導(dǎo)結(jié)合啟發(fā)式近似方法對陣列激勵以及位置進(jìn)行優(yōu)化選擇。最后，利用交替方向多乘子法(ADMM)實現(xiàn)聚焦位置、峰值旁瓣約束以及陣列激勵協(xié)同求解，通過改進(jìn)3維時域成像算法實現(xiàn)稀疏陣列3維成像。仿真模擬實驗結(jié)果顯示，該方法可以在滿足陣列天線輻射特性以及近場聚焦條件下，以更少的陣元數(shù)目獲得更低的旁瓣電平。此外，采用實測數(shù)據(jù)驗證稀疏陣列改進(jìn)3維時域成像算法高精度、高效率的優(yōu)勢。
- 近場成像 /
- 稀疏陣列 /
- 3維成像 /
- 交替方向多乘子法
Abstract: In active electrical scanning millimeter-wave security imaging, the uniform array antenna has the bottleneck of uncontrolled cost and high complexity, which is difficult to be widely applied in practices. To this end, a near-field focused sparse array design algorithm for high sparsity and low sidelobes is proposed in this paper. It applies an improved three dimensional (3D) time-domain imaging algorithm to achieve high-accuracy 3D reconstruction. Firstly, the near-field focusing sparse array antenna model is constructed by taking the near-field focusing position and peak sidelobe level as constraints, where the $ {\ell _p} $(0<p<1) norm of the weight vector regularization is established as the objective function. Secondly, by introducing auxiliary variables and establishing equivalent substitution models between sidelobe and focus position constraints and auxiliary variables, the problem of solving the array weight vector in the coupling of the objective function and complex constraints is developed. The model is simplified and solved through the idea of equivalent substitution. Then, the array excitation and position are optimized using a combination of complex number differentiation and heuristic approximation methods. Finally, the Alternating Direction Method of Multipliers (ADMM) is employed to achieve the focus position, peak sidelobe constraint, and array excitation in a cooperative manner. The sparse array 3D imaging is realized by improving the 3D time-domain imaging algorithm. The experimental results show that the proposed method is capable of obtaining lower sidelobe level with fewer array elements under the condition of satisfying the radiation characteristics of array antenna and near-field focusing. Applying raw millimeter-wave data, the advantages of sparse array 3D time-domain imaging algorithm are verified in terms of high accuracy and high efficiency.
- Near field imaging /
- Sparse array /
- Three-dimensional imaging /
- Alternating Direction Method of Multipliers (ADMM)

HTML全文

圖 1 圓柱體掃描毫米波安檢成像幾何示意圖

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圖 2 笛卡爾坐標(biāo)系下近場線性陣列天線模型

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圖 3 距離-高度維局部極坐標(biāo)網(wǎng)格

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圖 4 3維場景成像切片劃分

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圖 5 不同算法下天線方向圖對比

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圖 6 陣元位置分布及其對應(yīng)激勵值

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圖 7 輔助變量收斂曲線

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圖 8 均勻陣列與稀疏陣列成像結(jié)果對比

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圖 9 均勻陣列與稀疏陣列點目標(biāo)成像結(jié)果剖面圖

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1 稀疏陣列優(yōu)化算法

(1)初始化：$ {{\boldsymbol{\gamma}} ^{\mathrm{r}}}(0) $, $ {{\boldsymbol{\gamma}} ^{\mathrm{i}}}(0) $, $ {{\boldsymbol{\varsigma}} ^{\mathrm{r}}}(0) $, $ {{\boldsymbol{\varsigma}} ^{\mathrm{i}}}(0) $, $ {\boldsymbol{w}}(0) $，給定循環(huán)的迭代　次數(shù)$ K $, $ N $
(2) for $ i = 0,1, \cdots ,K $
步驟1　得到$ {q_0}(i + 1) $和$ {g_s}(i + 1) $通過式(12)–式(16)
步驟2　求解$ {\boldsymbol{w}}(i + 1) $
for $ k = 0,1, \cdots ,N $
(1)得到關(guān)于$ {\boldsymbol{w}} $非線性方程通過式(17)–式(21)
(2)確定$ {{\boldsymbol{w}}^{(k)}}(i + 1) $通過式(22)
End for $ k = N $
步驟3　通過式(23)更新$ {{\boldsymbol{\gamma}} ^{\mathrm{r}}}(i + 1) $, $ {{\boldsymbol{\gamma}} ^{\mathrm{i}}}(i + 1) $, $ {{\boldsymbol{\varsigma}} ^{\mathrm{r}}}(i + 1) $, $ {{\boldsymbol{\varsigma}} ^{\mathrm{i}}}(i + 1) $
end for $ i = K $
得到最終陣列權(quán)值向量的結(jié)果$ {\boldsymbol{w}} $

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表 1 圓周柱面陣列天線實測數(shù)據(jù)參數(shù)

雷達(dá)參數(shù)	數(shù)值	雷達(dá)參數(shù)	數(shù)值	雷達(dá)參數(shù)	數(shù)值
系統(tǒng)工作帶寬	6.5 GHz	方位/俯仰波束角	55°/55°	旋轉(zhuǎn)次數(shù)	314
工作頻率	27 GHz	單脈沖采樣點數(shù)	64	單次旋轉(zhuǎn)角度	0.2867°
目標(biāo)距離	0.4～0.8 m	陣元間距	0.0052 m	旋轉(zhuǎn)半徑	0.628 m

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表 2 均勻陣列與稀疏陣列點目標(biāo)成像結(jié)果剖面圖定量分析

點目標(biāo)高度向成像結(jié)果	峰值旁瓣比(dB)	高度向分辨率(mm)
均勻陣列成像	–24.27	7.76
稀疏陣列RMA成像	–16.82	7.76
稀疏陣列改進(jìn)3維時域成像	–20.32	7.76

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