一种基于I/Q失配的辐射源指纹提取方法

针对现有的辐射源识别方法的不足,利用辐射源自身I/Q失配指纹特征的可检测、可重现、不变的特性,提出一种基于I/Q失配的新型射频指纹特征提取方法.该方法是一种基于信号空间的指纹特征提取方法,它需要对信号的信噪比进行估计.综合考虑,采用基于特征值分解的信噪比估计方法.实验仿真结果表明,在相同的信噪比条件下,基于I/Q失配的射频指纹特征提取方法比双谱、Hilbert-Huang变换信号特征提取方法具有更加优越的射频指纹识别性能.     

辐射源个体识别研究综述

辐射源个体识别作为一种电子侦察技术,在战场敌我识别、目标态势感知、无线网络安全、频谱资源管理等军用和民用领域均具有重要应用价值。通过对国内外辐射源个体识别领域的研究进行系统性梳理,介绍了基于传统机器学习、深度学习、迁移学习等3类辐射源个体识别方法并分析了其优缺点。传统基于深度学习的辐射源个体识别方法假设训练数据与测试数据是同分布的,但在真实测试场景中,由于辐射源个体的中心频率、发送速率、接收距离以及接收机等都可能发生变化,导致训练数据与测试数据分布往往不同。迁移学习作为一种解决数据不同分布的技术逐渐成为辐射源个体识别领域的主流研究方向,重点介绍了基于迁移学习的辐射源个体识别方法,讨论了所提出方法未来的研究方向与面临的挑战,并给出相应的解决方案,同时对辐射源个体识别的未来研究方向进行了展望。     

载频稳定度在射频指纹识别中的应用研究

射频指纹识别是利用辐射源射频特征唯一性对其个体身份进行识别的技术,具有广阔的军民用应用前景。针对传统射频指纹特征稳定度不高、难以工程实现的问题,提出了一种基于辐射源频率偏移和相位噪声等多维频域特性的射频指纹识别方法。该方法以ADS-B信号为例,利用统计原理对频率偏移和相位噪声特征进行量化处理,并利用贝叶斯分类方法对信号进行分类以实现辐射源个体身份的识别。通过半实物生成和实采ADS-B信号数据两种方式对提出的算法进行实验验证。实验结果表明,该方法能够对同型号的辐射源进行有效区分,对真实环境下4个目标的识别正确率达到93.5%。     

基于深度学习的蓝牙射频指纹识别系统

蓝牙射频指纹具有难以伪造的优点，基于射频指纹的身份识别能有效提高网络的安全性。文中设计了一种基于深度学习网络的蓝牙射频指纹识别系统。首先，利用Hackrf One软件无线电平台和GNU Radio软件在蓝牙信号广播阶段采集多种蓝牙信标信号。其次，对蓝牙信号进行预处理，将预处理后的数据分为训练集与验证集。然后，使用MATLAB深度学习工具箱来设计长短期记忆网，利用训练数据集对各个网络进行训练，得到蓝牙射频指纹识别网络。最后，利用验证集对上述网络进行测试和分析。当迭代次数为300时，网络对3种蓝牙信标的射频指纹识别的准确率均达到80%以上。     

雷达辐射源个体识别综述

雷达辐射源个体识别通过提取个体特征来辨识雷达个体,是电子对抗领域的热点研究方向。近年来随着深度学习的飞速发展及其在各领域的成功应用,基于深度学习的雷达辐射源个体识别成为焦点。虽然研究多年,成果丰富,但目前尚缺少关于该方向全面、细致的综述。基于此,该文从雷达辐射源个体特征机理分析、基于手工特征的识别方法、基于深度学习的识别方法以及数据集构建4个方面着手,对雷达辐射源个体识别开展系统的综述工作,并对当前现状和未来方向进行总结与展望,旨在推动雷达辐射源个体识别理论和方法研究的新发展。     

基于生成式对抗网络和多模态注意力机制的扩频与常规调制信号识别方法

针对低信噪比条件下的扩频与常规调制信号分类精度低的问题,该文提出一种基于生成式对抗网络(GAN)、卷积神经网络(CNN)和长短期记忆(LSTM)网络的多模态注意力机制信号调制识别方法。首先生成待识别信号的时频图像(TFIs),并利用GAN实现TFIs降噪处理;然后将信号的同相正交数据(I/Q data)与TFIs作为模型输入,并搭建基于CNN的TFIs识别支路和基于LSTM的I/Q数据识别支路;最后,在模型中添加注意力机制,增强I/Q数据和TFIs中重要特征对分类结果的决定作用。实验结果表明,该文所提方法相较于单模态识别模型以及其它基线模型,整体分类精度有效提升2%～7%,并在低信噪比条件下具备更强的特征表达能力和鲁棒性。     

深度学习及其在人脸识别中的应用进展

深度学习是目前受到广泛关注的机器学习方法。通过构建深度神经网络且使用深度学习算法学习适合模式识别和分类的高层次特征是目前模式识别领域热门研究方向,特别是在人脸识别领域已经取得了目前最好的成果。文章首先介绍了深度学习概述,然后分析了基于深度学习的人脸识别方法及识别模型并对识别模型进行了分类。最后讨论了应用深度学习进行人脸识别还存在的问题。该文拟对将深度学习应用于人脸识别的研究人员有所帮助。     

雷达辐射源信号分选研究进展

雷达辐射源信号分选是雷达信号侦察的关键技术之一,同时也是战场态势感知的重要环节。该文系统梳理了雷达辐射源信号分选的主流技术,从基于脉间调制特征、基于脉内调制特征、基于机器学习的雷达辐射源信号分选3个角度阐述了目前雷达辐射源信号分选工作的主要研究方向及进展,并重点阐释了基于深度神经网络、数据流聚类等最新分选技术的原理与特点。最后,对现有雷达辐射源信号分选技术的不足进行了总结并对未来趋势进行了预测。      

基于深度学习的无线通信接收方法研究进展与趋势

随着无线通信应用边界的不断扩展,无线通信应用环境也日趋复杂多样,面临射频损伤、信道衰落、干扰和噪声等负面影响,给接收端恢复原始信息带来挑战。借鉴深度学习方法在计算机视觉、模式识别、自然语言处理等领域取得的研究成果,基于深度学习的无线通信接收技术受到学术界和产业界的广泛关注。首先阐述了国内外基于深度学习无线通信接收技术的研究现状;接着概述了信号大数据背景下无线通信接收所面临的技术挑战,并提出基于深度神经网络的无线通信智能接收参考架构;最后探讨了信号大数据背景下无线通信智能接收方法的发展趋势。为基于深度学习无线通信技术的研究和发展提供借鉴。     

基于领域自适应的动态噪声辐射源个体识别

现有基于深度神经网络的辐射源识别算法受训练场景限制,当待测信号与训练数据集的信道环境噪声不一致时,网络的识别性能严重退化。为了克服该问题,本文提出一种基于迁移学习的辐射源个体识别算法。该算法结合领域自适应的思想,建立优化模型将不同信噪比下信号的特征对齐,使在特定信噪比下训练的神经网络学习到与信道噪声无关的射频指纹特征,实现对其他信噪比下信号的高准确率识别。仿真实验结果表明,提出的算法显著提升了基于深度神经网络的辐射源个体识别算法在动态噪声条件下的准确率,在待识别信号信噪比下降4 dB的情况下,准确率提升了45.18%。      

零中频数字通信发射机的射频指纹时域基带建模

射频指纹(RFF)来源于发射机电路设计的差异和生产过程中硬件电路的制造容差,是一种新兴的设备身份识别和认证技术。对射频指纹产生机理进行建模是深入研究射频指纹技术的基础。本文根据一种通用的零中频数字通信发射机结构分析了各环节对射频指纹的影响,并建立了对应的射频指纹时域基带模型。此外,归纳总结了一系列通信标准的若干重要时域参数的容差,并着重研究了LTE标准下,正交相移键控(QPSK)和十六进制正交振幅调制(16-QAM)两种典型调制方式的最大均方根误差向量幅度(RMS EVM)。最后,通过理论推导和Matlab仿真给出了直流偏置、同相/正交(I/Q)增益不平衡、I/Q正交偏移误差、I/Q滤波器偏差、振荡器相噪和功放非线性参数的上下界,并分析了各种射频指纹参数临界情形下星座图的变化,为射频指纹提取和识别技术的研究提供了合理的参数指导。     

基于2D实值离散Gabor变换的SAR原始数据压缩

该文采用时频分析的方法,结合合成孔径雷达(SAR)原始数据的线性调频特性,提出了一种基于2D实值离散Gabor变换的SAR原始数据压缩方法。该方法对I,Q两路原始数据进行分块,先对每一块进行二维实值离散Gabor变换(2D-RDGT),在时频平面内对各频率平面根据方差进行比特分配,然后采用BAQ量化各频率平面。用同一块数据进行压缩解压缩成像实验并与已有的方法进行比较,实验结果表明该文提出的方法在数据域和图像域的性能都优于已有的方法。     

A survey on deep learning for textual emotion analysis in social networks

Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. Various Deep Learning (DL) methods have developed rapidly, and they have proven to be successful in many fields such as audio, image, and natural language processing. This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research. In this paper, we provide an overview of TEA based on DL methods. After introducing a background for emotion analysis that includes defining emotion, emotion classification methods, and application domains of emotion analysis, we summarize DL technology, and the word/sentence representation learning method. We then categorize existing TEA methods based on text structures and linguistic types: text-oriented monolingual methods, text conversations-oriented monolingual methods, text-oriented cross-linguistic methods, and emoji-oriented cross-linguistic methods. We close by discussing emotion analysis challenges and future research trends. We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.     

Frequency-Selective I/Q Mismatch Calibration of Wideband Direct-Conversion Transmitters

The current trend in building low-cost yet flexible radio transceivers is to use the so-called direct-conversion principle, which is based on complex (I/Q) up- and down conversions. Such transceivers are, however, sensitive to mismatches between the I and Q branches. These mismatches are unavoidable in any practical implementation, and result in finite attenuation of the mirror frequencies. In addition to the mirror-frequency interference problem, I/Q mismatches can severely compromise the performance of power amplifier linearization techniques based on pre-distortion. The effects of these impairments are becoming more pronounced as higher order modulated waveforms and/or more wideband multichannel signals are used. This brief focuses on digital-signal-processor-based I/Q mismatch calibration in wideband direct-conversion transmitters, assuming the challenging case of frequency-dependent I/Q mismatch. First, a novel widely linear (WL) calibration structure is introduced, suitable for frequency-dependent calibration. Then, two alternative principles for calibration parameter estimation are proposed. The first estimation approach stems from second-order statistics of complex communication signals, while the second technique is based on WL least-squares model fitting. Both estimators are shown by simulations to yield very good calibration performance. The obtainable performance is further assessed using laboratory RF signal measurements.     

Monostatic multi-source direction finding based on I/Q radio frequency data

At present, many researchers study methods for estimating signal sources through direction finding. The use of phase interferometry for directional applications is common. This method measures the phase through the phase-locked loop module, and the detection bandwidth is limited. We propose to extract the phase information of signals from the I/Q data by electromagnetic measurement. In this paper, One-dimensional phase interferometer is expanded to two-dimensional antenna array. Use the equilateral triangle antenna array to increase the spatial dimension of the direction finding antenna array, and to avoid the phase ambiguity. Through the calculation of associated phase difference to achieve all-directional angle detection. The angle incidence of multi-signal estimation method in single station is proposed in this work. The estimated phase difference is converted into the calculated the duty cycle of the phase waveform. The experiment result show that the method based on the I/Q data can quickly achieve multi-signal source direction finding.     

Decision-Directed Channel Estimation and High I/Q Imbalance Compensation in OFDM Receivers

Direct-conversion architectures suffer from the mismatch between the In-phase (I) and the Quadrature-phase (Q) branches, commonly called I/Q imbalance. Even low I/Q imbalances imply poor performance of Orthogonal Frequency Division Multiplexing (OFDM) systems. In this paper, we propose a new algorithm that uses both training and data symbols in a decision-directed fashion to jointly estimate the channel and compensate for high receiver I/Q imbalance. Simulation results show that our method can compensate for high I/Q imbalance values and also estimate a frequency selective channel.     

基于时域RF-DNA的功率放大器射频指纹识别

射频(RF)功率放大器是辐射源射频指纹特征产生的关键器件之一,是射频指纹(RFF)产生机理和个体识别的重要突破口。设计一种功率放大器射频指纹提取实验方法,利用时域射频独特原生属性(RF-DNA)方法成功提取了功率放大器的射频指纹,并对RF-DNA指纹进行了可视化处理。研究结果表明,功率放大器的射频指纹主要反映在幅度失真特性上,利用瞬时幅度生成的时域RF-DNA指纹能够实现对放大器个体的分类,在信噪比大于12 dB时,分类正确率在91%以上。可视化后,能直观观察RF-DNA指纹及不同功率放大器之间统计特征的相似性和差异性。     

I/Q Imbalance Compensation Using a Nonlinear Modeling Approach

In-phase/quadrature (I/Q) imbalance is one of the main sources of distortion in RF modulators. In this paper, a dual-input nonlinear model based on a real-valued Volterra series is proposed for compensation of the nonlinear frequency-dependent I/Q imbalance. First, different sources of distortion are identified from experimental measurements, then a dual-input nonlinear I/Q imbalance model is developed. Further, the inverse model is used for I/Q imbalance compensation. Finally, the performance of the I/Q imbalance compensator is evaluated with both simulations and experiments. In comparison with previously published results, the proposed I/Q imbalance compensator shows significantly improved performance. Thus, we prove that a complete nonlinear I/Q imbalance compensation can minimize the effects of the RF modulator in high-performance digital communication systems.      

宽带回波信号的非线性幅相压缩性能分析

为了在雷达回波数据压缩中较好地保留目标的相位信息,研究了基于非线性离散余弦变换(DCT)和自适应小波贪婪(AWG)算法的幅相压缩性能。运用上述2种算法对宽带回波信号分别进行了正交I/Q压缩和幅相压缩实验。通过性能指标CSNR(压缩信噪比)和CPSD(压缩相位标准差),比较了I/Q压缩和幅相压缩的压缩性能。结果表明,同传统的I/Q压缩相比,幅相压缩能很好地保留目标的幅相信息;上述2种幅相压缩方法在不同信噪比情况下各有优势,非线性DCT幅相压缩适宜于压缩低信噪比宽带回波信号,基于AWG算法的幅相压缩则适宜于压缩高信噪比回波信号。