一种深度学习的立体阵波达方向估计方法

针对常规波束形成主瓣宽且目标分辨能力低的问题，提出一种基于深度卷积神经网络的波达方向估计方法。算法使用常规波束形成计算二维空间功率谱，将预处理后的空间功率谱图输入深度卷积神经网络。该文利用神经网络学习解卷积映射关系，输出主瓣宽度更窄的空间功率谱图，从而实现高分辨率二维波达方向估计。该算法对阵列结构没有限制，适用于立体阵。仿真结果表明该文方法在不同目标个数、快拍数及信噪比参数下均能准确估计目标方向。该文方法目标分辨能力优于常规波束形成方法。在低快拍情况下，目标方向估计误差低于自适应波束形成方法。

A direction of arrival estimation method for stereo array based on deep learning

Aiming at the problem of wide main lobe and low target resolution in conventional beamforming, a direction of arrival estimation method based on deep convolution neural network is proposed. The algorithm calculates the two-dimensional spatial power spectrum using conventional beamforming, and feeds the preprocessed spatial power spectrum into a deep convolutional neural network. In this paper, a neural network is used to learn the deconvolution mapping relationship and output a spatial power spectrum with a narrower main lobe width, thereby realizing high-resolution two-dimensional direction of arrival estimation. The algorithm has no restrictions on the array structure and is suitable for stereo arrays. Simulation results show that this method can accurately estimate the target direction under different target number, snapshot number and signal-to-noise ratio parameters. The target resolution capability of the proposed method is better than that of conventional beamforming. In the case of low snapshot, the target direction estimation error is lower than that of the adaptive beamforming method.