Complex-Valued Convolutional Neural Networks Design and Its Application on UAV DOA Estimation in Urban Environments

Direction-of-arrival(DOA)estimation is an important task in many unmanned aerial vehicle(UAV)applications.However,the complicated electromagnetic wave propagation in urban environments substantially deteriorates the performance of many conventional model-driven DOA estimation approaches.To alleviate this,a deep learning based DOA estimation approach is proposed in this paper.Specifically,a complex-valued convolutional neural network(CCNN)is designed to fit the electromagnetic UAV signal with complex envelope better.In the CCNN design,we construct some mapping functions using quantum probabilities,and further analyze some factors which may impact the convergence of complex-valued neural networks.Numerical simulations show that the proposed CCNN converges faster than the real convolutional neural network,and the DOA estimation result is more accurate and robust.

基于复值卷积神经网络样本精选的极化SAR图像弱监督分类方法

针对物体框标注样本包含大量异质成分的问题,该文提出了一种基于复值卷积神经网络(CV-CNN)样本精选的极化SAR(PolSAR)图像弱监督分类方法。该方法首先采用CV-CNN对物体框标注样本进行迭代精选,并同时训练出可直接用于分类的CV-CNN。然后利用所训练的CV-CNN完成极化SAR图像的分类。基于3幅实测极化SAR图像的实验结果表明,该文方法能够有效剔除异质样本,与采用原始物体框标注样本的传统全监督分类方法相比可以获得明显更优的分类结果,并且该方法采用CV-CNN比采用经典的支持矢量机(SVM)或Wishart分类器性能更优。