Effects of Signal Spectrum Bandwidth on Different PMD Compensation Feedback Methods

We compared efficiencies of different PMD compensation feedback methods against transmission signal bandwidth, including NRZ, RZ, CRZ format under various duty cycles. We found that the critical factor determining the efficiency of PMD compensation is not the modulation format, but the spectral bandwidth of the transmission signal.

Application of Wavelet Packet Energy Spectrum to Extract the Feature of the Pulse Signal

小浪包表现为小浪分析跟随的一种新有点多尺度的分析技术。基础和小浪包分析方法的实现算术在这份报纸被描述。小浪的一条新应用程序途径包装了方法从散布角度的精力提取脉搏信号的特征是 expatiated。微芯片由使用小浪包分析方法使脉搏信号使量子化并且分析处理并且判定是方便的。有点，实验在实验室被模仿，并且实验证明基于小浪包装精力光谱分析提取脉搏信号的特征是一个方便、精确的方法。

A novel dense spectrum correction algorithm for extracting vibration signals in internal combustion engine and its application

The algorithm of dense spectrum correction has been raised and proved based on the correction of discrete spectrum by fast Fourier transform.The result of simulation shows that such algorithm has advantages of high accuracy and small amount of calculation.The algorithm has been successfully applied to the analysis of vibration signals from internal combustion engine.To calculate discrete spectrum,fast Fourier transform has been used to calculate the discrete spectrum by the signals acquired by the sensors on the oil pan,and the signal has been extracted from the mixed signals.

Bayesian decision-based pattern recognition on spectrum signal of metal transfer modes

A software system of pattern recognition on spectrum signal of metal transfer mode has been developed using Visual Basic under Windows environment. On the basis of the coincidence relation between the spectrum signal and metal transfer mode, according to the geometrical pattern feature of the spectrum signal, several key characteristic parameters are extracted. The correspondent recognition function and a minimum distance classifier have been constructed based on Bayesian decision theory. The results show that using this system, the metal transfer mode of MIG, MAG, CO2 welding can be recognized automatically which provides the basis for automatically controlling of the metal gas arc welding metal transfer.

Non-Supervised Learning for Spread Spectrum Signal Pseudo-Noise Sequence Acquisition

An idea of estimating the direct sequence spread spectrum（DSSS） signal pseudo-noise（PN） sequence is presented. Without the apriority knowledge about the DSSS signal in the non-cooperation condition, we propose a self-organizing feature map（SOFM） neural network algorithm to detect and identify the PN sequence. A non-supervised learning algorithm is proposed according the Kohonen rule in SOFM. The blind algorithm can also estimate the PN sequence in a low signal-to-noise（SNR） and computer simulation demonstrates that the algorithm is effective. Compared with the traditional correlation algorithm based on slip-correlation, the proposed algorithm＇s bit error rate（BER） and complexity are lower.

Design of a Weak Signal Detecting Circuit in the Infrared Spectrum Absorbed Type Gas Sensor

In the infrared spectrum absorbed type gas concentration sensor,voltage signal obtained from the two-channel thermopile infrared detector TPS2534 is very weak.In order to solve this problem,the authors have established the structure of the sensor and designed weak signal detecting circuit of the sensor based on infrared spectrum absorption principle,differential de-noising principle and weak signal detecting principle.The authors have made experiments using CH4 gas.The results show that the circuit can remove noise effectively and detect weak electrical signal obtained from the detector.

SENSORING DROPLET SPRAY TRANSFER IN MIG WELDING BASED ON ARC SPECTRUM SIGNAL

The method to detect droplet transfer by means of arc spectrum, while the experiment sets, testing principle and data processing procedure,are presented. The experiment and analysis results show that arc spectrum signal can be utilized to detect and measure the transfer procedure, the transfer modes and the transfer parameters. The arc spectrum signal enjoys excellent quality with high signal amplitude. Each transfer mode has its specific typical signal mode, and the pulse outline corresponds to an integrated transferring procedure of one droplet. All these features of arc spectrum signal can be easily applied hi the control of transfer procedure,the identification and stabilization of transfer mode and the measurement of transfer parameters.

基于窄带频谱分析的电力线载波通信干扰过滤技术

电力线路载波通信受到背景噪声、脉冲噪声等方面干扰因素影响,导致通信信号质量下降,为此提出一种基于窄带频谱分析的通信干扰过滤技术。首先构建近似模型分析通信信号特征,利用马尔可夫链模拟信道上脉冲噪声状态。然后基于窄带频谱分析算法,在信号频谱周期图下,利用正交数据窗分离直接频谱,筛选估计方差以避免频谱泄漏事件的发生。利用傅里叶变换计算特征频谱,估计信号能量的集中特性,分析通信信号,平衡信号振幅和干扰抑制。最后通过时频峰值实施干扰滤波,有效保证电力线载波通信信号质量。经过实验数据分析证明,所提技术的载波干扰幅值最小值为1.1 dB,载波通信信号误码率平均值为1.9%,方法耗时平均值为3.8 s。

Harmonicity Spectrum

Perceiving harmonic information (especially weak harmonic information) in time series has important scientific and engineering significance. Fourier spectrum and time-frequency spectrum are commonly used tools for perceiving harmonic information, but they are often ineffective in perceiving weak harmonic signals because they are based on energy or amplitude analysis. Based on the theory of Normal time-frequency transform (NTFT) and complex correlation coefficient, a new type of spectrum, the Harmonicity Spectrum (HS), is developed to perceive harmonic information in time series. HS is based on the degree of signal harmony rather than energy or amplitude analysis, and can therefore perceive very weak harmonic information in signals sensitively. Simulation examples show that HS can detect harmonic information that cannot be detected by Fourier spectrum or time-frequency spectrum. Acoustic data analysis shows that HS has better resolution than traditional LOFAR spectrum.

宜宾乡镇级密集地震监测台网建设及地震信号分析

在宜宾建设地震监测台网,布置在地震多发区域的八个台站形成乡镇级密集地震监测台网。与传统强震台阵不同,各台站均包含地面测点和邻近多层学校建筑楼层测点。共获取136次地震诱发的地面及楼层信号共约2000条,根据信号特点大致分为震中区、近区、远区和超远区四类信号。震中区和近区信号数据量最多,为强地面和楼层加速度数据,其中震中距小于10 km的震中区信号地面及建筑楼层信号共56条。选取了代表性的数据,分析了地面平均反应谱特征,给出了地面加速度峰值随震中距的衰减曲线。还对地面和/或楼层信号峰值相对较小的超远区信号、远区信号作了讨论。

轴承转子系统快变过程振动响应的高精度频谱分析

针对高速轴承转子系统的启停过程,研究平稳和非平稳信号的高精度频谱分析方法,利用比例校正法对平稳信号频谱分析后的频率、幅值、相位进行了校正,建立一种将t时空域非平稳信号转变为t~2时空域平稳信号的方法,并基于平稳信号的比例校正法对其进行频谱分析,最后返回到t时空域获得某时刻的频域参数。仿真分析结果表明:该方法可以准确提取平稳信号的谱特征,也能较精确地提取非平稳信号特定时刻的频率、幅值和相位,提取结果精度较好,有效解决轴承转子系统快变过程的谱分析特征提取问题,为动力学特性分析提供了新思路。

针对冲击性故障信号的谱融合特征提取算法

利用盲解卷积方法在时域中进行故障信号特征提取时,常会出现多个信号混淆分离结果,但以往的研究中只强调了分离的部分,而很少对分离后的信号进行进一步的处理,给实际应用造成不便。这里在盲解卷积和谱融合的基础之上,使用核改进的模糊c均值聚类算法,针对机械故障信号的脉冲特性,提出一种针对冲击性故障信号处理的实用型算法。计算机仿真实验证实了该算法的有效性。此算法优化了以往的聚类筛选方法,可以有效排除反卷积后诸多无用信号的干扰,将故障脉冲信号的特征准确提取出来,能提高故障诊断的效率。

DroneRFa:用于侦测低空无人机的大规模无人机射频信号数据集

为研究与发展反无人机检测识别技术,该文公开了一个名为DroneRFa的大规模无人机射频信号数据集。该数据集使用软件无线电设备探测无人机与遥控器相互通信的射频信号,包含城市户外场景下运动无人机信号9类、城市室内场景下信号15类以及背景参照信号1类。每类数据有不少于12个片段,每个片段包含1亿个以上的采样点。数据采集覆盖了3个ISM无线电频段,记录无人机多频通信的真实活动。该数据集具有详细的无人机户外飞行距离和工作频段标注,以前缀字符结合二进制编码的形式方便用户灵活访问所需数据。此外,该文提供了基于频谱可视统计特征和基于深度学习表征的两种无人机识别方案,以验证数据集的可靠和有效性。

基于轨道角动量谱分解的多通道水声通信及系统实现

水声通信能够实现信息的长距离传输,在深海探索和军事领域发挥重要作用.然而,由于换能器带宽和声波波长的影响,水声通信的发展与应用受到传输衰减大和信道容量低以及频谱利用率不足的限制.本文结合复用涡旋声束(AV)和轨道角动量(OAM)谱分解技术,研究了基于环形收发阵列的多通道水声通信方法,并系统实现.基于发送数据的ASCII编码,利用全息信号的幅度和相位调制,驱动环形换能器阵列发射沿轴传输的多通道OAM复用涡旋声束,并通过环形换能器阵列的信号采样和OAM谱分解完成复用涡旋声束的OAM模态解码,实现了高效的并行数据传输.构建了8声源环形收发阵列和全息相控系统,开展了6通道水声通信研究,成功实现了复用涡旋声场的实验测量和OAM解码,并与传统单通道二进制串行数据通信进行性能比较.理论和实验结果证明,基于OAM谱分解的通信通道数由发射阵列的阵元数N决定,其解码精度由发射信号的相位精度和接收阵列的阵元数决定;阵元数N≥1的大阵列系统在满OAM模式复用条件下的传输速率约为传统水声通信的N倍.本研究表明基于OAM谱分解的多通道通信可大幅提高信道容量和频谱利用率,为其在水声通信中的实际应用提供了一种实用方案.

基于SVD-K-means算法的软扩频信号伪码序列盲估计

针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。

基于多带加权包络谱的轴箱轴承故障诊断

为增强复杂噪声干扰下轴箱轴承故障检测的鲁棒性,基于循环谱分析并考虑轴承故障信息分布差异和阈值降噪,对轴箱轴承故障诊断的包络谱构造方法进行了研究.首先,提出频域信噪比作为轴承故障信息量化的新测度,用于评估谱相干中不同谱频带内的轴承故障相关信息;其次,构造以谱频率为变量的故障特征信息分布函数,并自适应确定信息阈值来辨识谱相干中故障信息丰富和干扰噪声主导的谱频率分量,进一步基于故障特征信息分布函数和信息阈值设计权重函数;最后,由谱相干和权重函数生成融合多带信息的多带加权包络谱,通过分析谱中的轴承故障特征频率来检测轴箱轴承的不同故障.铁路轴箱轴承实验数据的分析结果表明:相比于基于谱相干的典型包络谱方法,多带加权包络谱能够在复杂噪声干扰下准确识别轴箱轴承的外圈、滚动体和内圈故障,并能取得更高的性能量化指标(频域信噪比和负熵).

多途混响环境中声扩频信号线性与相关性分析

为分析浅海多途信道中声扩频信号的相关性和线性,建立了计入频率衰减与海底损耗的浅海Pekeris波导虚源法计算模型;对浅海中的脉冲信号与扩频信号进行了仿真计算,通过混响室试验初步研究了多途混响环境中扩频信号的线性。仿真结果显示:虚源法和简正波法的频域解一致,得到了与简正波理论相同的低频模式频散现象,验证了虚源法适用于浅海理想波导、Pekeris波导中的低频问题;当发生模式频散,低频扩频信号的相关性能强于同扩频参数的高频信号。试验结果表明:低频脉冲的时域波形发生畸变、展宽时,同频率的扩频信号能准确测出反射信号的几何时延,证明了低频多途效果仍可看作每个“路径”信号的线性叠加。

太阳光谱仪近红外光电探测系统设计

针对星载太阳光谱仪近红外波段信号微弱、易受外部环境干扰等特点,设计了一种基于InGaAs红外单元探测器的锁相放大光电探测系统。分析了探测系统的噪声特性,发现抑制前置放大器的电路噪声对于降低系统噪声至关重要。优化了前置放大器PCB布局布线,采用保护环+保护层+过孔防护等设计,加强了输入光电流信号保护与屏蔽。利用卤钨灯模拟在轨情况下的太阳光谱强度,进行了地面试验。结果表明,探测电路对入射光强度的响应线性度可达99.95%;测试波段各波长信噪比较上一代太阳光谱仪均有显著提升。

战场电磁频谱智能认知技术

未来军事力量竞争逐步向空天领域转移,外层空间已经成为战场信息的重要媒介。随着信息化水平不断提高,电磁空间作为空天战场信息的主要载体愈加复杂,对电磁频谱认知能力提出了更高要求。针对现代战场电磁频谱环境快速时变、信号多样的挑战,提出了空天战场电磁频谱智能认知系统架构,以及基于该架构的全并行瞬时大带宽信号处理、高精度多信号参数估计、复合特征调制识别和多域融合干扰识别技术,有效解决了空天平台复杂电磁环境高动态大带宽信号的认知难题。

基于循环谱的单通道时频混叠信号识别

随着通信技术的发展和电磁环境的日益复杂,通信领域中的时频混叠信号频繁显现。一方面,这类信号会干扰通信系统的正常接收;另一方面,当混叠信号中引入虚假信号时,接收器极有可能接收虚假信号,从而导致获取欺骗信息。因此,有必要对混叠信号做出准确的识别,确保通信的可靠性,从而提出了一种基于循环谱特征的时频混叠信号识别算法。该算法基于非合作方式设计,不依赖于特定的调制参数,具备较优的普适性,并且测试表明,该算法对时频混叠信号的识别准确率优于传统算法。