We present the convolution neural networks (CNNs) to achieve the localization of near-field sources via the symmetric double-nested array (SDNA). Considering that the incoherent near-field sources can be separated in the frequency spectrum, we first calculate the phase difference matrices and consider the typical elements as the inputs of the networks. In order to guarantee the precision of the angle-of-arrival (AOA) estimation, we implement the autoencoders to divide the AOA subregions and construct the corresponding classification CNNs to obtain the AOAs of near-field sources. Then, we construct a particular range vector without the estimated AOAs and utilize the regression CNN to obtain the range parameters of near-field sources. The proposed algorithm is robust to the off-grid parameters and suitable for the scenarios with the different number of near-field sources. Moreover, the proposed method outperforms the existing method for near-field source localization.

中文翻译：

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用于通过对称双嵌套阵列定位近场源的卷积神经网络

我们提出了卷积神经网络 (CNN) 以通过对称双嵌套阵列 (SDNA) 实现近场源的定位。考虑到非相干近场源可以在频谱中分离，我们首先计算相位差矩阵并将典型元素视为网络的输入。为了保证到达角（AOA）估计的精度，我们实现了自动编码器来划分AOA子区域并构建相应的分类CNN以获得近场源的AOA。然后，我们在没有估计的 AOA 的情况下构建一个特定的距离向量，并利用回归 CNN 来获得近场源的距离参数。该算法对离网参数具有鲁棒性，适用于不同近场源数量的场景。此外，所提出的方法优于现有的近场源定位方法。