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《通信與信息網(wǎng)絡(luò)學報（英文）》
>Complex-Valued Convolutional Neural Networks Design and Its Application on UAV DOA Estimation in Urban Environments
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 ana-lyze 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.
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