ABSTRACT

In this paper, an intelligent direction finding (DF) architecture based on hybrid neural network is proposed. Firstly, the array outputs of DF system are multiplied by the corresponding ideal array manifold and a high-dimensional preprocessed feature vector is derived. In the next, the preprocessed feature is straightly fed into the auto-encoder. The auto-encoder outputs are then used as the input of the cascaded deep residual neural network in order to extract spatial spectral of sources. Finally, the direction-of-arrival (DOA) results are derived according to the locations of the normalised spectral peaks. There are two advantages with our proposed network architecture. One is the DF estimation accuracy is independent on the array aperture size when the number of elements is fixed. And the other is that the proposed network structure and processing procedures can be directly generalised into unknown scenarios but having higher angle resolution capacity at the same time. Finally, extensive numerical experiments illustrate the correctness and potential advantages of the proposed deep hybrid neural network.

摘要

本文提出了一种基于混合神经网络的智能测向（DF）架构。首先，将DF系统的阵列输出乘以相应的理想阵列流形，得到一个高维预处理特征向量。接下来，将预处理后的特征直接输入自动编码器。然后将自动编码器输出用作级联深度残差神经网络的输入，以提取源的空间谱。最后，根据归一化光谱峰值的位置推导出到达方向（DOA）结果。我们提出的网络架构有两个优点。一是当元件数量固定时，DF估计精度与阵列孔径大小无关。另一个是所提出的网络结构和处理过程可以直接推广到未知场景，但同时具有更高的角度分辨率能力。最后，大量的数值实验说明了所提出的深度混合神经网络的正确性和潜在优势。