This paper introduces an enhanced deep learning-based (DL) antenna selection approach for optimum sparse linear array selection for direction-of-arrival (DOA) estimation applications. Generally, the antenna selection problem yields a combination of subarrays as a solution. Previous DL-based methods designated these subarrays as classes to fit the problem into a classification problem to which a convolutional neural network (CNN) is employed to solve it. However, these methods sample the combination set randomly to reduce computational cost related to the generation of training data, and it often leads to sub-optimal solutions due to ill-sampling issues. Hence, in this paper, we propose an improved DL-based method by constraining the combination set to retain the hole-free subarrays to enhance the method’s performance and sparse subarrays rendered. Numerical examples show that the proposed method yields sparser subarrays with better beampattern properties and improved DOA estimation performance than conventional DL techniques.

本文介绍了一种增强的基于深度学习（DL）的天线选择方法，用于到达方向（DOA）估计应用的最佳稀疏线性阵列选择。通常，天线选择问题产生子阵列的组合作为解决方案。以前的基于DL的方法将这些子数组指定为类，以将问题适合到分类问题中，使用卷积神经网络（CNN）对其进行求解。然而，这些方法随机采样组合集以减少与训练数据的生成相关的计算成本，并且由于不良采样问题，它常常导致次优解决方案。因此，在本文中，我们通过约束组合集以保留无孔子阵列以增强方法的性能和稀疏呈现的子阵列，提出了一种改进的基于DL的方法。