基于深度卷积神经网络的DOA估计

针对现有均匀线阵远场窄带非相干多目标估计算法对低信噪比、少快拍情况适应性差、运算复杂度高，以及现有深度学习方法难以有效提取数据复值特征的问题，提出基于深度卷积神经网络的波达方向估计方法。该方法将波达方向估计问题转换为阵列输出协方差矩阵到目标到达角度的逆映射问题，利用阵列输出协方差矩阵的Hermitian特性，提取其上三角阵的实部、虚部及相位特征，构造网络的输入数据，搭建包含三维卷积层的深度卷积神经网络用来提取数据特征，网络的标签对应目标的到达角度，从而实现多个信源的波达方向估计。试验仿真表明:该方法可以充分提取空间特征，提高波达方向估计精度并降低算法复杂度。所提方法在低信噪比、少快拍数的情况下，其估计精度明显优于MUSIC、ESPRIT以及ML算法。

A DOA Estimation Based on Deep Convolutional Neural Network

Aimed at the problems thatthe existing Uniform Linear Array (ULA) far-field narrowband non-coherent multi-target estimation algorithms is poor in adaptability to low Signal-to-Noise Ratio (SNR), small snapshots in adaptability, high in computational complexity, and existing Deep Learning (DL) approaches are difficult to effectively extract the complex-valued features of data,a Direction of Arrival (DOA) estimation method based on Deep Convolution Neural Network (DCNN) is proposed . This method is to transform the DOA estimation problem into an inverse mapping problem from the array output covariance matrix to the target DOA, and to utilize the Hermitian characteristic of the array output covariance matrix for extracting the real part, imaginary part, and phase characteristics of an upper triangular array,building input data of a network, and building a deep convolutional neural network with a three-dimensional convolution layer to extract data features, and the labels of the network correspond to the DOAs,realizing the DOA estimation of multiple sources. Theexperimental simulations show that the method can fully extract spatial features, improve DOA estimation accuracy and reduce the complexity of the algorithm.Under condition of low SNR and small snapshots, the estimation accuracy of the proposed method is significantly better than that of the MUSIC, the ESPRIT and the ML algorithms.