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基于卷积神经网络的自适应波束形成

Adaptive Beamforming Based on Convolutional Neural Network
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摘要 提出一种基于卷积神经网络的自适应波束形成方法,旨在通过自适应调整接收波束方向以提高通道中信号的信噪比。采用同时多波束接收信号的概念,对阵列天线接收信号以不同的相位加权并相加,即依据自适应多波束形成多通道接收。利用神经网络的非线性处理能力,在阵列接收不同来波方向信号时可以对其实现自适应波束形成。使用波束形成方式设计标签,通过比较输出信号的信噪比相对于输入信号信噪比的增益以评价该方法的有效性。仿真有效提高了信号的信噪比,接收阵列可以同时接收多个波束信号并按各输入信号的入射角度不同在不同的通道输出对应信号。 An adaptive beamforming method based on convolutional neural network(CNN)is proposed,which are aimed at enhancing the signal-to-noise ratio(SNR)of signals in channels by adaptively adjusting the directions of receiving beams.Employing the concept of simultaneous multibeam signal reception,the signals received by the array antenna are weighted and added with different phases,that is,multiple channels reception is performed according to adaptive multi-beamforming.Utilizing the nonlinear processing capability of neural networks,adaptive beamforming can be realized when the array receives signals of different incoming wave directions.The beamforming method is used to design labels,and the effectiveness of this method is evaluated by comparing the gain that the SNR of the output signal relative to that of input signal.The simulation results effectively improve the SNR of signals.The receiving array can receive multiple beam signals at the same time and output corresponding signals in different channels according to the different incident angles of each input signal.
作者 唐元博 蒋伊琳 李帅 李虎 TANG Yuanbo;JIANG Yilin;LI Shuai;LI Hu(Harbin Engineering University,Harbin 150001,China;AVIC United Technology Center for Electromagnetic Spectrum Collaborative Detection and Intelligent cognition,Harbin 150001,China;Beijing Aerospace Long March Aircraft Research Institute,Beijing 100076,China)
出处 《舰船电子对抗》 2024年第5期47-50,共4页 Shipboard Electronic Countermeasure
关键词 阵列信号处理 自适应波束形成 深度卷积神经网络 array signal processing adaptive beamforming deep convolutional neural network
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