针对现有深度学习方法难以有效利用阵列回波信号的复值相位信息这一问题,文中提出了一种基于复值卷积网络的均匀线阵波达方向(DOA)估计方法,旨在提高DOA估计精度并增强低信噪比条件下对多信源参数估计的适应能力。该方法利用实际阵列输出信号协方差矩阵的Hermitian特性,以其上三角数据作为复值网络的输入,以对应的理想数据作为标签,学习得到信号理想协方差矩阵的第一行,再结合其Hermitian和Toeplitz特性,重构该理想矩阵;最后采用子空间类算法进行DOA估计。仿真结果表明:相比传统子空间类和实值卷积网络算法,该算法在低信噪比下具有更高的估计精度。

Aiming at the problem that the existing deep learning methods are difficult to effectively use the complex-valued phase information of array echo signals, a uniform linear array direction of arrival (DOA) estimation method based on complex-valued convolutional network is proposed in this paper to improve the accuracy of DOA estimation and enhance the adaptability of multi-source parameter estimation under the condition of low signal-to-noise ratio. This method uses the Hermitian characteristics of the actual array output signal covariance matrix, takes its upper triangular data as the input of the complex-valued network, takes the corresponding noiseless data as the label, learns the upper triangle of the signal ideal covariance matrix, and then reconstructs the ideal matrix combined with its Hermitian and Toeplitz characteristics. Finally, subspace algorithm is used to estimate DOA. Simulation results show that compared with the traditional subspace class and real-valued convolutional network algorithm, this algorithm has higher estimation accuracy under low signal-to-noise ratio.

TN957.51

国家自然科学基金创新研究群体项目