基于双卷积自编码器的自适应波束形成  

Adaptive Beamforming Based on Dual Convolutional Autoencoder

作　　者：蒋伊琳 李帅 郑沛 唐元博 JIANG Yilin;LI Shuai;ZHENG Pei;TANG Yuanbo(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China;AVIC United Technology Center for Electromagnetic Spectrum Collaborative Detection and Intelligent cognition,Harbin 150001,China;State Key Laboratory of Experimental Physics and Computational Mathematics,Beijing 100876,China)

机构地区：[1]哈尔滨工程大学,哈尔滨150001 [2]航空工业电磁频谱协同探测与智能认知联合技术中心,哈尔滨150001 [3]试验物理与计算数学国家级重点实验室,北京100876

出　　处：《电子与信息学报》2025年第2期510-518,共9页Journal of Electronics & Information Technology

基　　金：国防科技基础加强计划(2019-JCJQ-ZD-067-00)。

摘　　要：在低信噪比环境下,阵列天线获取空域信号的来波方向极其困难,导致一般的波束形成方法无法准确形成正对入射信号的波束。针对上述问题,该文提出了一种基于双卷积自编码器的盲接收自适应波束形成(Dual Convolutional AutoEncoder-Adaptive Beamforming,DCAE-ABF)方法,该方法在基于大量空域统计信息的情况下,以时域-频域联合条件作为约束,利用两个独立的卷积自编码器(CAE)分别对阵列接收信号与辐射源信号进行特征提取,并使用深度神经网络(DNN)将两个CAE的特征编码进行连接,构建DCAE网络,实现在低信噪比环境下,面对未知频率和来波方向的入射信号时,也能够自适应形成正对入射信号的波束,达到盲接收的效果。仿真实验结果表明,在低信噪比环境下,单信号与双信号入射时所带来的信噪比增益均高于常规波束形成(CBF)方法与基于最小均方误差的自适应波束形成(Minimum Mean Square Error-Adaptive BeamForming,MMSE-ABF)方法,以及基于卷积神经网络的自适应波束形成方法(Convolutional Neural Networks-Adaptive BeamForming,CNN-ABF),且该增益在入射信号频率、角度变化时仍具有良好的稳定性。Objective Most traditional beamforming techniques and adaptive beamforming methods rely on reference signals.These methods require prior knowledge of the signal frequency and Direction of Arrival(DOA)at the array for beamforming.However,in low Signal-to-Noise Ratio(SNR)environments,obtaining the frequency and DOA of the incident signals is extremely challenging.This difficulty leads to significant performance degradation in reference-signal-based beamforming,limiting its applicability in tasks such as electronic reconnaissance and electronic countermeasures in low SNR conditions.This paper addresses the challenge of enabling antenna arrays to perform adaptive beamforming for incident signals with unknown frequencies and DOAs in low-SNR environments.Methods This paper proposes a Dual Convolutional AutoEncoder-Adaptive Beamforming(DCAE-ABF)method for blind reception.The approach leverages dual Convolutional Autoencoders(CAEs)to extract features from both the array-received signal and the radiation source signal,utilizing extensive air-domain statistical information with joint time-frequency domain constraints.A Deep Neural Network(DNN)connects the feature encodings from the two CAEs to construct the DCAE network.This method enables adaptive beamforming in low SNR environments,even when the incident signal’s frequency and DOA are unknown,facilitating blind reception.Results and Discussions Simulation results demonstrate that the proposed DCAE-ABF method can rapidly and accurately adjust the beam direction for incident signals with unknown frequencies and directions of arrival in a low SNR environment,effectively orienting the beam towards the incident signals for optimal reception.This method improves the output signal’s SNR,with the SNR gain significantly exceeding that of traditional beamforming techniques(Fig.4,Fig.6).Furthermore,the SNR gain achieved by this method remains stable even when the frequency and angle of the incident signal vary(Fig.5).Conclusions This paper presents an adaptive beamforming metho

关 键 词：自适应波束形成 卷积自编码器 盲波束形成 信噪比增益 

分 类 号：TN911.7[电子电信—通信与信息系统]