基于多幀相位增強(qiáng)的米波雷達(dá)低仰角目標(biāo)DOA估計方法
doi: 10.11999/JEIT190432 cstr: 32379.14.JEIT190432
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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
Low-elevation DOA Estimation for VHF Radar Based on Multi-frame Phase Feature Enhancement
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National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
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摘要:
針對米波雷達(dá)低仰角目標(biāo)的DOA估計問題，該文提出一種新的基于多幀相位特征增強(qiáng)方法，所提方法可以有效解決低仰角條件下陣列接收信號中直達(dá)信號相位特征模糊問題，進(jìn)而提高DOA估計精度。通過學(xué)習(xí)多幀原始數(shù)據(jù)的相位分布特征與理想環(huán)境下直達(dá)波信號的相位分布特征之間的復(fù)雜映射關(guān)系，有效削弱多徑信號引起的相位誤差，將增強(qiáng)后的相位信息與原始的幅度信息進(jìn)行數(shù)據(jù)重組，并利用已有的超分辨算法進(jìn)行DOA估計。通過計算機(jī)仿真實驗和實測數(shù)據(jù)驗證，該文所提方法在DOA估計性能以及泛化能力上優(yōu)于基于物理驅(qū)動的MUSIC算法以及數(shù)據(jù)驅(qū)動的基于特征反演和基于支持向量回歸的兩種估計方法。
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關(guān)鍵詞:
- 雷達(dá)信號處理 /
- 來波方向估計 /
- 多幀相位增強(qiáng) /
- 米波雷達(dá)
Abstract:For the DOA estimation problem of low-elevation target of VHF radar, a new multi-frame phase feature enhancement based method is proposed, which solves effectively the phase feature ambiguity of direct signal, and thus improves the accuracy of DOA estimation. By learning the complex mapping relationship between the phase distribution of the multi-frame data and ideal phase distribution of the direct signal, the fuzzy phase information is enhanced and is used to reconstruct a new data matrix with original amplitude information. The DOA is estimated by conventional methods using new data matrix, which effectively improves the DOA estimation accuracy of the low-elevation target. The effectiveness of proposed method is validated by computer simulation experiments and real data, and it shows higher accuracy compared with physics-driven methods including MUSIC method and state-of-the-art data-driven method including feature reversal and Support Vector Regression (SVR).
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表 1 深度神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)配置
網(wǎng)絡(luò)結(jié)構(gòu) 激活函數(shù) 學(xué)習(xí)率 初始化方式 ${{x}} \times 1024 \times 1024 \times 1024 \times {{o}}$ ReLU 10–4 高斯隨機(jī)初始化 
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表 2 深度卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)配置
網(wǎng)絡(luò)結(jié)構(gòu) 卷積核大小 池化層大小 激活函數(shù) 學(xué)習(xí)率 初始化方式 2層卷積層 $3 \times 1 \times 15$ $1 \times 3$ ReLU 10–4 高斯隨機(jī) 3層全連接層 $3 \times 15 \times 30$ 初始化
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表 3 有效點數(shù)占比(%)
方法 DBF SSMUSIC 3幀DNN 5幀DNN 7幀DNN 3幀CNN 5幀CNN 7幀CNN 占比 1.8 0.2 95.6 93.5 89.8 85.1 81.1 72.7
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