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

作     者：Bai Shi Xian Ma Wei Zhang Huaizong Shao Qingjiang Shi Jingran Lin Bai Shi;Xian Ma;Wei Zhang;Huaizong Shao;Qingjiang Shi;Jingran Lin

作者机构：University of Electronic Science and Technology of ChinaChengdu 611731China Science and Technology on Electronic Information Control LabaratoryChengdu 610036China Peng Cheng LaboratoryShenzhen 518055China Tongji UniversityShanghai 200092China 

出 版 物：《Journal of Communications and Information Networks》 (通信与信息网络学报（英文）)

年 卷 期：2020年第5卷第2期

页      面：130-137页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 082503[工学-航空宇航制造工程] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：direction-of-arrival(DOA)estimation complex-valued convolutional neural network(CCNN) unmanned aerial vehicle(UAV) 

摘      要：Direction-of-arrival(DOA)estimation is an important task in many unmanned aerial vehicle(UAV)***,the complicated electromagnetic wave propagation in urban environments substantially deteriorates the performance of many conventional model-driven DOA estimation *** alleviate this,a deep learning based DOA estimation approach is proposed in this ***,a complex-valued convolutional neural network(CCNN)is designed to fit the electromagnetic UAV signal with complex envelope *** 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 *** simulations show that the proposed CCNN converges faster than the real convolutional neural network,and the DOA estimation result is more accurate and robust.