Wang Jiangyi, Liu Min, Zeng Xinwu, Hua Xiaoqiang
College of Meteorology and Oceanography, National University of Defence Technology, Changsha 410073, China.
College of Computer, National University of Defence Technology, Changsha 410073, China.
Entropy (Basel). 2020 Aug 28;22(9):949. doi: 10.3390/e22090949.
Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5-2 dB on simulated data sets and semi-physical simulated data sets.
卷积神经网络由于其层次结构和强大的特征提取能力，在许多视觉任务中具有强大的性能。对称正定（SPD）矩阵在视觉分类中受到关注，因为它具有出色的学习适当统计表示和区分具有不同信息的样本的能力。本文提出了一种基于谱卷积特征的深度神经网络信号检测方法。在该方法中，利用从卷积神经网络提取的局部特征来构建SPD矩阵，并使用针对SPD矩阵的深度学习算法来检测目标信号。本研究应用了两种卷积神经网络模型提取的特征图。基于该方法，信号检测已成为样本中信号的二分类问题。为了证明该方法的有效性和优越性，使用了模拟和半物理模拟数据集。结果表明，在低信杂比（SCR）下，与基于深度神经网络的谱信号检测方法相比，该方法在模拟数据集和半物理模拟数据集上可获得0.5 - 2 dB的增益。
