Zhang Wenqiong, Huang Yiwei, Tong Jianfei, Bao Ming, Li Xiaodong
Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2021 Apr 14;21(8):2767. doi: 10.3390/s21082767.
Low-frequency multi-source direction-of-arrival (DOA) estimation has been challenging for micro-aperture arrays. Deep learning (DL)-based models have been introduced to this problem. Generally, existing DL-based methods formulate DOA estimation as a multi-label multi-classification problem. However, the accuracy of these methods is limited by the number of grids, and the performance is overly dependent on the training data set. In this paper, we propose an off-grid DL-based DOA estimation. The backbone is based on circularly fully convolutional networks (CFCN), trained by the data set labeled by space-frequency pseudo-spectra, and provides on-grid DOA proposals. Then, the regressor is developed to estimate the precise DOAs according to corresponding proposals and features. In this framework, spatial phase features are extracted by the circular convolution calculation. The improvement in spatial resolution is converted to increasing the dimensionality of features by rotating convolutional networks. This model ensures that the DOA estimations at different sub-bands have the same interpretation ability and effectively reduce network model parameters. The simulation and semi-anechoic chamber experiment results show that CFCN-based DOA is superior to existing methods in terms of generalization ability, resolution, and accuracy.
低频多源到达角（DOA）估计对于微孔径阵列来说一直具有挑战性。基于深度学习（DL）的模型已被引入到这个问题中。一般来说，现有的基于DL的方法将DOA估计表述为多标签多分类问题。然而，这些方法的精度受到网格数量的限制，并且性能过度依赖于训练数据集。在本文中，我们提出了一种基于非网格DL的DOA估计方法。其主干基于循环全卷积网络（CFCN），由空间频率伪谱标记的数据集进行训练，并提供网格上的DOA提议。然后，开发回归器以根据相应的提议和特征估计精确的DOA。在这个框架中，通过循环卷积计算提取空间相位特征。通过旋转卷积网络将空间分辨率的提高转化为增加特征维度。该模型确保不同子带处的DOA估计具有相同的解释能力，并有效减少网络模型参数。仿真和半消声室实验结果表明，基于CFCN的DOA在泛化能力、分辨率和精度方面优于现有方法。
