Low-elevation DOA Estimation for VHF Radar Based on Multi-frame Phase Feature Enhancement
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摘要:
针对米波雷达低仰角目标的DOA估计问题，该文提出一种新的基于多帧相位特征增强方法，所提方法可以有效解决低仰角条件下阵列接收信号中直达信号相位特征模糊问题，进而提高DOA估计精度。通过学习多帧原始数据的相位分布特征与理想环境下直达波信号的相位分布特征之间的复杂映射关系，有效削弱多径信号引起的相位误差，将增强后的相位信息与原始的幅度信息进行数据重组，并利用已有的超分辨算法进行DOA估计。通过计算机仿真实验和实测数据验证，该文所提方法在DOA估计性能以及泛化能力上优于基于物理驱动的MUSIC算法以及数据驱动的基于特征反演和基于支持向量回归的两种估计方法。
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 深度神经网络结构配置
网络结构 激活函数 学习率 初始化方式 ${{x}} \times 1024 \times 1024 \times 1024 \times {{o}}$ ReLU 10–4 高斯随机初始化 
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表 2 深度卷积神经网络结构配置
网络结构 卷积核大小 池化层大小 激活函数 学习率 初始化方式 2层卷积层 $3 \times 1 \times 15$ $1 \times 3$ ReLU 10–4 高斯随机 3层全连接层 $3 \times 15 \times 30$ 初始化
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