Bistable Stochastic Resonance Enhanced 4-ary PAM Signal Detection under Low SNR

To boost the performance of 4-ary pulse amplitude modulated(PAM) at low signal-to-noise ratio(SNR), bistable stochastic resonance(BSR) system is introduced into digital communications system and get a reliable signal detection scheme. In this paper, we first analyse BSR system for different amplitudes of 4-ary PAM signals. The steadystate of the bistable system will be statistically distinct, and the feasibility of the proposed detection scheme is confirmed. On this basis, we present a detailed study on steady-state transitions of the BSR system, and an explicit expression of the bistable system parameters is derived. By setting the bistable system parameters, bistable system, 4-ary PAM signal, and noise reach the resonance state, and the BSR-based detection scheme is implemented. Moreover, we derive an analytical expression to calculate the symbol error rate(SER) of 4-ary PAM signals with the BSR-based detection under additive white Gaussian noise(AWGN). Finally, the simulation results validate that BSR-based detection scheme can improve the detection performance while efficiently reducing the symbol error rate.

Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR

Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters.The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio(SNR).Then considering the rotor rate variance of UAV in the complex motion state,the cepstrum method is improved to extract the rotation rate of the UAV,and the blade length can be intensively estimated.The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved.However,the computation complexity is higher and the heavier computation burden is required.

Blind parameter estimation of pseudo-random binary code-linear frequency modulation signal based on Duffing oscillator at low SNR

Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBC-LFM)signals require prior knowledge,are computationally complex,and exhibit poor performance at low signal-to-noise ratios(SNRs).To overcome these problems,a blind parameter estimation method based on a Duffing oscillator array is proposed.A new relationship formula among the state of the Duffing oscillator,the pseudo-random sequence of the PRBC-LFM signal,and the frequency difference between the PRBC-LFM signal and the periodic driving force signal of the Duffing oscillator is derived,providing the theoretical basis for blind parameter estimation.Methods based on amplitude method,short-time Fourier transform method,and power spectrum entropy method are used to binarize the output of the Duffing oscillator array,and their performance is compared.The pseudo-random sequence is estimated using Duffing oscillator array synchronization,and the carrier frequency parameters are obtained by the relational expressions and characteristics of the difference frequency.Simulation results show that this blind estimation method overcomes limitations in prior knowledge and maintains good parameter estimation performance up to an SNR of-35 dB.

塔里木盆地塔西南山前带地震技术攻关进展及发展方向

塔里木盆地西南坳陷复杂山地山前带处于西昆仑造山带与天山造山带的交汇处,强烈挤压作用造成地下构造高陡破碎,与复杂多变的近地表地震地质条件共同造成地震原始数据信噪比极低,构造成像困难,是国内少有的超高难度探区。受地震资料品质影响,该区油气勘探长期未取得实质性突破。近年来持续开展了地震勘探攻关,采集方面加强近地表调查及激发接收参数优选,优化观测系统设计,开展线束地震及高密度宽方位三维地震采集技术攻关;处理方面探索多重联合约束初至层析反演、黄土区“黑三角”带强能量噪声压制、真地表速度建模和深度偏移成像等技术。经过技术攻关,地震资料品质不断得到提高,发现和落实了一批钻探目标,首次在侏罗系砂岩和石炭—二叠系碳酸盐岩取得两项重大突破,打开了塔西南山前带油气勘探新局面。

低SNR海天线提取算法

针对海天线SNR低且有一定倾角难于提取的问题,提出一种新的海天线提取算法。在预处理阶段,利用Laplacian模板对海面的波纹进行增强,然后使用顺序滤波与数学形态学相结合的方法增加海面波纹的面积与灰度,预处理后增大了海面与天空的灰度差,海天线的SNR提升70%以上;最后,设计了一种斜线查找矩阵,通过对图像行扫描生成该矩阵,矩阵最大值的横纵坐标即为海天线的位置和角度。实测数据的试验结果验证了该方法的有效性且算法运算简单,易于工程应用。创新点在于使用模板运算与形态学相结合的方法提高了海天线SNR并设计了斜线查找矩阵可以简单地定位海天线的位置和角度,减少了计算复杂度。

基于循环谱的去模糊调制识别算法

针对当前调制识别算法在低信噪比下识别率低和循环谱应用中由于部分信号谱图相似而性能下降的问题,提出基于二维循环谱灰度图的去模糊调制识别算法。针对谱图特征相似的信号构建了二维循环谱灰度图模板库,通过与模板库进行谱图匹配将信号分流成两部分:谱图相似信号和谱图可区分信号。在信号分流基础上提出差异化识别方法,针对谱图可区分信号,通过构建基于二维循环谱灰度图的卷积神经网络(CSG-Net)完成识别;针对谱图相似信号,借助多通道学习深度神经网络(MCLDNN)完成识别。实验结果表明,提出的算法综合了不同网络的优势,提升了网络整体的识别性能,在-10 dB信噪比时依然有接近70%的识别率。

低信噪比下基于深度学习TCNN-MobileNet的调制识别

将深度学习算法应用于调制识别任务是近年来通信领域的一个研究热点,但现有方法存在网络复杂度高、硬件要求高、在低信噪比(SNR)下识别准确率不高等问题。结合离散小波变换方法,提出一种基于双路卷积神经网络级联可分离卷积网络(TCNN-MobileNet)的调制识别方法。首先通过小波变换对数据进行预处理,将信号作为输入传送到双路卷积神经网络中进行不同维度的特征提取;然后通过融合层进行特征融合并送入轻量级神经网络MobileNetV1中,进行调制识别模型训练;最后通过全连接层进行11种调制识别的分类输出。在公开数据集RML2016.10a上的实验结果表明,在-20dB的低SNR下TCNN-MobileNet的识别准确率可达88.71%,在18dB的高SNR下识别准确率可达96.66%,SNR在-20~18dB范围内时平均识别准确率为88.37%,相比于ResNet18、ResNet34等经典网络架构提升了约35%。TCNN-MobileNet识别方法在保证识别精度不变的情况下能够降低训练参数量以及网络训练时间,有效简化网络架构,降低对硬件设备的要求,对轻量级神经网络在调制识别中的应用具有借鉴意义。

采用Mash2-1架构的高精度Sigma-Delta ADC设计

鉴于模数转换器(ADC)在芯片和混合信号处理领域举足轻重的作用,为探索进一步提高模数转换器精度的可能性,满足当今应用系统对模数转换精度的新需求,设计一款基于Mash2-1级联架构的sigma-delta ADC。设计采用TSMC 180 nm CMOS工艺,在完成调制器电路的同时,使用Verilog代码方式实现了配套的误差消除以及数字抽取滤波器。对调制器进行了建模及非理想因素的分析,并通过使用斩波调制技术等手段对电路的非理想性进行优化。经仿真验证,结果表明,本设计在3.3 V电源、27℃工作环境、TT典型工艺条件下,表现出优异的信噪比,总谐波失真等性能指标也较理想,适用于高精度、低失真的应用场合。

空间通信载波多普勒频偏捕获的两阶段稀疏算法

在空间通信中,信号面临远距离传输和高动态相对运动,其中,远距离传输带来很低信噪比(SNR,signal-to-noise ratio),而高动态相对运动则引起载波高动态多普勒频偏。为解决低信噪比问题,传统捕获方法需要长时间累积很多接收信号。但是,在长时间累积过程中,高动态多普勒频偏会导致严重的能量扩散问题。针对上述问题,提出了一种两阶段稀疏(TSS, two-stage-sparse)算法来捕获载波多普勒频偏。该算法首先利用粗捕获结果构造粗稀疏搜索范围,然后选择若干个较大元素来构建精稀疏搜索范围,最后搜索最大元素作为捕获结果。由于稀疏范围仅覆盖很窄的频率区间,该算法能够滤除更多的噪声干扰,从而辅助信号元素成为最大元素。理论分析和仿真结果也表明,所提TSS算法能够显著提高多普勒频偏的捕获概率。

基于Radon变换的宽带信号多域参数同时测量方法

实际电子监测设备的作战环境复杂多变,辐射源个数、工作频率、工作带宽、到达角度等多域参数信息对于实现工作场景的实时精确感知至关重要。提出一种基于Radon变换的宽带信号多域参数同时测量方法,实现多个宽带信号多域重点工作参数的同时快速测量,大大提升低信噪比条件下监测目标特性的多维实时感知能力。该方法结合数字信道化结构及二重FFT算法实现全侦收频段及全侦收角度的空时域联合功率聚焦,并生成作战场景的空时二维频谱幅度图,利用Radon变换算法实现不同映射角度的线性积分运算,构建合适的检测门限,推导计算全场景多目标多参数测量结果。实验显示,该方法可以有效提升弱信号的参数测量精度,实现多目标多域参数同时测量。

基于FRFT的低信噪比LFM信号参数快速估计算法

基于分数阶傅里叶变换(Fractional Fourier Transform,FRFT)对线性调频(Linear Frequency Modulated,LFM)信号参数进行估计,问题关键是确定FRFT最佳阶数,根据误差迭代思想提出新的参数估计算法,该算法利用归一化带宽和旋转角的转化关系,由估计误差推算角度差值,有效降低了运算量,不需要调频斜率正负的先验信息,改进的对数搜索算法可以进一步提高参数估计结果的稳定性和可靠性。仿真结果表明,信噪比在-8 dB以上时该方法在高效率的前提下仍具有良好的参数估计性能,平均估计误差在1%以内,估计结果接近Cramer-Rao下限,满足工程实时处理需求。

Blind carrier frequency estimation for phase shift keying signals in low signal to noise ratio

In order to solve the problem of carrier frequency blind estimation of PSK signals in electronic reconnaissance, a new estimation method was proposed. The phase shift keying（PSK） signal was divided into several overlapping intervals which had equal length, and the spectrum concentration measures of every interval were extracted by the FFT. And then, using the grid-density clustering, the spectrum concentration measures were classified into two categories, the narrowband spectrum interval and the wideband spectrum interval. The narrowband spectrum interval was regarded as the characteristic class. The spectrums of the characteristic class were accumulated to estimate the carrier frequency of PSK signal. The proposed method had avoided the non linear operation in the traditional PSK signal carrier frequency estimation algorithm. Thus, the signal to noise ratio （SNR） threshold was remarkably decreased. Moreover, the proposed method did not need the prior knowledge of the signal, which was suitable to the electronic reconnaissance occasion. Experimental results had verified the validity of the proposed estimation method in low SNR.

Novel Frame Shift and Integral Technique for Enhancing Low-Light-Level Moving Images

A novel frame shift and integral technique for the enhancement of low light level moving image sequence is introduced. According to the technique, motion parameters of target are measured by algorithm based on difference processing. To obtain spatial relativity, images are shifted according to the motion parameters. As a result, the processing of integral and average can be applied to images that have been shifted. The technique of frame shift and integral that includes the algorithm of motion parameter determination is discussed, experiments with low light level moving image sequences are also described. The experiment results show the effectiveness and the robustness of the parameter determination algorithm, and the improvement in the signal-to-noise ratio （SNR） of low light level moving images.

Variational Wavelet Ensemble Empirical(VWEE)Denoising Method for Electromagnetic Ultrasonic Signal in High‑Temperature Environment with Low‑Voltage Excitation

Low excitation voltage for an electromagnetic acoustic transducer(EMAT)is necessary for the petrochemical equipment and facilities inspection,which work at high-temperatures,to avoid potential explosion.However,low excitation voltage causes low signal-to-noise ratio(SNR)signals that are difficult to extract features,especially in a high-temperature environment,which causes high noise.In this study,a denoising method called the variational wavelet ensemble empirical(VWEE)method was proposed by combining the advantages of the variational modal decomposition(VMD),wavelet threshold(WT)denoising,and ensemble empirical mode decomposition(EEMD)methods.To validate the VWEE method,EMAT signals obtained in the temperature range of 25 to 700°C were analyzed.The results show that,compared with VMD,WT and empirical mode decomposition denoising methods,the SNR of proposed method is improved more than two times.The VWEE method dramatically improved the SNR of a high-temperature EMAT signal and enhanced the accuracy of defect echos extraction.

微光条件下遥感器像移补偿机构设计及控制方法

夜间成像时,遥感成像地面目标的辐亮度可低至白天的10–6,在提升弱信号获取能力的各种措施中,通过长曝光时间提升信噪比是一种直接而有效的解决方式,但是在低轨遥感任务中其实现却受到极大限制。为完成探测目标凝视,文章提出通过光路中的像移补偿机构实现光轴稳定的解决方案,补偿了相机相面与地物的相对运动,在曝光时间内使探测器相面景物与地物景物保持相对静止,从而实现灵活微光成像模式;具体阐述了微光条件下遥感器像移补偿机构的设计和控制方法,对其原理及设计进行了详细分析,并进行仿真及试验验证,结果表明所设计的像移补偿系统可将图像抖动补偿至0.2像元之内,为低轨遥感微光成像系统提供了优异的解决途径。

基于并行架构的宽带扩频信号捕获技术研究

为解决大动态低信噪比环境下,由于宽带扩频信号速率过快,使得载波多普勒和伪码多普勒较大导致无法成功捕获信号的问题,提出一种基于并行架构的捕获方法;利用并行载波NCO产生本地载波,与接收信号进行下变频混频以对载波和伪码多普勒进行补偿,利用并行伪码NCO产生本地码进行内码滑动相关和外码匹配滤波,经过二次捕获进一步估计载波多普勒和伪码相位;仿真结果表明,在62 dBHz的载噪比下,符号速率为1 Mbps,载波多普勒搜索范围为±800 kHz,载波多普勒变化率为±100 kHz/s时,这种方法可以实现宽带扩频信号捕获,并且在符号信噪比为2 dB时,捕获概率为99%,捕获时间为0.9009 s;使用该捕获方法,可以降低宽带扩频信号捕获复杂度、提高捕获效率,为大动态低信噪比环境下宽带扩频信号的捕获提供了一种有效的捕获方法。

低场核磁共振仪器振铃抑制新方法及其电路实现

加快天线残余能量释放以减弱天线振铃信号有利于缩短低场核磁共振仪器的回波间隔(TE),从而提高快弛豫组分的测量分辨率和信噪比(SNR).而天线Q值对能量的发射效率和泄放速度起着相反的作用.为此,我们首先设计了一种新型Q转换电路,在保证发射效率的同时,可以大大缩短能量泄放时间.在此基础上,应用了一种优化的脉冲序列以弥补传统相位交替对脉冲序列(PAPs)不能消除90脉冲振铃的缺陷,通过相位循环的方法进一步提高了信噪比.最后,在2 MHz岩心分析仪上测试了新型Q转换电路,当天线Q值降为发射期间的约1/5时,天线恢复时间由280.0μs降为18.2μs;而且,使用新型Q转换电路和优化的脉冲序列后,TE=60μs时,可以有效获得快弛豫组分的T2信号.

低信噪比下非冗余阵列的无网格DOA估计

波达方向(direction of arrival,DOA)是阵列信号处理模型中的非线性参数,当信噪比较低时,其估计值会偏离真实值。为了降低无网格DOA估计方法中该问题的阈值,介绍了一种基于无网格的基于协方差的稀疏迭代估计(sparse iterative covariance-based estimation,SPICE)方法。引入了最大似然求根多重信号分类(maximum likelihood root multiple signal classification,ML-Root-MUSIC)来计算DOA,使用最大似然准则来选择根,可以降低阈值并获得更好的分辨率特性。在原始无网格SPICE的优化问题中加入了负熵项,使得无网格SPICE的均方根误差曲线更接近于Cramer-Rao下界。最后,蒙特卡罗仿真实验验证了所提方法在低信噪比非冗余阵列情况下的优越性。

基于多普勒变化率估计的载波跟踪算法

为了改善卫星通信系统中,在低信噪比下对大动态的信号进行载波跟踪时,动态应力误差和热噪声引起的相位抖动使得信号解调损失明显的现象,提出了一种基于多普勒变化率估计的载波跟踪算法;首先通过延迟相关快速傅里叶变换(FFT)的方法和FFT分析的方法对接收信号的多普勒变化率和多普勒频率进行估计,然后利用估计值对接收信号进行多普勒变化率以及频率补偿,最后利用三阶锁相环对补偿后的信号进行跟踪,并在考虑符号速率的基础上,对该算法进行改进并仿真;仿真结果表明,在63 dBHz的载噪比下,符号速率为1 Mbps,跟踪变化率为150 kHz/s时,使用该方法可以实现载波稳定跟踪,并且解调损失在0.3 dB以内;该算法可以降低载波跟踪复杂度、提高跟踪精度,为接收机载波跟踪模块的改进提供了依据。

低轨卫星高动态信号捕获算法研究

低轨卫星数量呈井喷式增长,未来低轨卫星天基测控将是提升海量卫星管控能力的有效手段。针对低轨卫星星载终端接收天基测控信号时面临卫星运动速度快、多普勒频率大、可视时间短等因素引起的信号快速捕获难题,论文研究了PMF-FFT(partial match filter-fast Fourier transform)高动态捕获算法对低轨卫星系统的适用性,分析了捕获多普勒范围的决定因素,捕获对信噪比的要求,以及平均捕获时间的相关因素,并通过Matlab仿真进行验证。结果表明,在信噪比-25 dB以上时,能够捕获到多普勒频移为95 kHz的信号,平均捕获时间在200 ms以内。针对不同信号体制和多普勒频率,需要调整匹配滤波器长度和FFT点数,该捕获算法适用于低轨卫星星载终端。