An adaptive microwave photonic angle-of-arrival(AOA) estimation approach based on a convolutional neural network with a bidirectional gated recurrent unit(BiGRU-CNN) is proposed and demonstrated.Compared with the prev...An adaptive microwave photonic angle-of-arrival(AOA) estimation approach based on a convolutional neural network with a bidirectional gated recurrent unit(BiGRU-CNN) is proposed and demonstrated.Compared with the previously reported AOA estimation methods based on phase-to-power mapping,the proposed method is unnecessary to know the frequency of the signal under test(SUT) in advance.The envelope voltage correlation matrix is obtained from dual-drive Mach–Zehnder modulator(N-DDMZM,N > 2) optical interferometer arrays first,and then AOA estimations are performed on different frequency signals with the aid of BiGRU-CNN.A three-DDMZM-based experiment is carried out to assess the estimation performance of microwave signals at three different frequencies,and the mean absolute error is only 0.1545°.展开更多
基金supported by the National Natural Science Foundation of China (Nos.61801498 and 62075240)the National Key Research and Development Program of China (No.2020YFB2205804)。
文摘An adaptive microwave photonic angle-of-arrival(AOA) estimation approach based on a convolutional neural network with a bidirectional gated recurrent unit(BiGRU-CNN) is proposed and demonstrated.Compared with the previously reported AOA estimation methods based on phase-to-power mapping,the proposed method is unnecessary to know the frequency of the signal under test(SUT) in advance.The envelope voltage correlation matrix is obtained from dual-drive Mach–Zehnder modulator(N-DDMZM,N > 2) optical interferometer arrays first,and then AOA estimations are performed on different frequency signals with the aid of BiGRU-CNN.A three-DDMZM-based experiment is carried out to assess the estimation performance of microwave signals at three different frequencies,and the mean absolute error is only 0.1545°.
