Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100039, China.
Sensors (Basel). 2020 Apr 17;20(8):2296. doi: 10.3390/s20082296.
Estimating directions of arrival (DOA) without knowledge of the source number is regarded as a challenging task, particularly when coherence among sources exists. Researchers have trained deep learning (DL)-based models to attack the problem of DOA estimation. However, existing DL-based methods for coherent sources do not adapt to variable source numbers or require signal independence. Herein, we put forward a new framework combining parallel DOA estimators with Toeplitz matrix reconstruction to address the problem. Each estimator is constructed by connecting a multi-label classifier to a spatial filter, which is based on convolutional-recurrent neural networks. Spatial filters divide the angle domain into several sectors, so that the following classifiers can extract the arrival directions. Assisted with Toeplitz-based method for source-number determination, pseudo or missed angles classified by the estimators will be reduced. Then, the spatial spectrum can be more accurately recovered. In addition, the proposed method is data-driven, so it is naturally immune to signal coherence. Simulation results demonstrate the predominance of the proposed method and show that the trained model is robust to imperfect circumstances such as limited snapshots, colored Gaussian noise, and array imperfections.
在不知道源数的情况下估计到达方向（DOA）被认为是一项具有挑战性的任务，特别是当源之间存在相干性时。研究人员已经训练了基于深度学习（DL）的模型来解决 DOA 估计问题。然而，现有的基于 DL 的相干源方法不适应可变的源数或需要信号独立性。在此，我们提出了一种新的框架，将并行 DOA 估计器与 Toeplitz 矩阵重建相结合来解决这个问题。每个估计器都是通过将多标签分类器连接到基于卷积-递归神经网络的空间滤波器来构建的。空间滤波器将角度域划分为几个扇区，以便以下分类器可以提取到达方向。借助基于 Toeplitz 的源数确定方法，可以减少估计器分类的伪或丢失角度。然后，可以更准确地恢复空间谱。此外，所提出的方法是数据驱动的，因此自然对信号相干性具有免疫力。仿真结果表明了所提出方法的优势，并表明训练后的模型对有限快照、有色高斯噪声和阵列不完善等不完美情况具有鲁棒性。
