Source number detection is a critical problem in array signal processing. Conventional model-driven methods e.g., Akaikes information criterion and minimum description length, suffer from severe performance degradation when the number of samples is small or the signal-to-noise ratio is low. In this letter, we exploit the model-aided based deep neural network to estimate the source number. Specifically, we propose two eigenvalue based networks, i.e., a regression network (ERNet) and a classification network (ECNet), for source number detection, where the eigenvalues of the received signal covariance matrix and the source number are used as the input and the label of the networks, respectively. Furthermore, ERNet and ECNet can be easily generalized to handle coherent sources by adopting, e.g., the forward-backward spatial smoothing technique. Numerical results are included to showcase the remarkable improvements of ERNet and ECNet over the existing methods.

中文翻译：

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用于源编号检测的模型辅助深度神经网络

源数检测是阵列信号处理中的一个关键问题。传统的模型驱动方法，例如 Akaikes 信息准则和最小描述长度，当样本数量较少或信噪比较低时，性能会严重下降。在这封信中，我们利用基于模型辅助的深度神经网络来估计源编号。具体来说，我们提出了两个基于特征值的网络，即回归网络 (ERNet) 和分类网络 (ECNet)，用于源编号检测，其中接收信号协方差矩阵和源编号的特征值用作输入和分别为网络的标签。此外，ERNet 和ECNet 可以很容易地通过采用例如前向-后向空间平滑技术来处理相干源。