基于自适应滤波的ASK解调方法及性能

研究了一种基于自适应滤波算法的振幅移位键控 (ASK)信号的方法。采用常用的最小均方误差(LMS)自适应算法 ,研究了自适应解调方法对ASK信号的解调及其性能。计算机模拟结果表明 :自适应ASK解调比传统的ASK解调性能优越 ,便于用数字信号处理技术实现。     

基于色噪声背景下线性滤波器组检测性能的研究

随着5G新空口信号处理技术的广泛应用,为了提高大规模组网中基站(天线)滤波器检测系统的检测性能,本文提出了一种基于色噪声背景下对信号进行检测判决的线性滤波器组(LFB)检测系统,即在传统匹配滤波器检测系统中加入白化滤波器级联组构成线性滤波器组检测系统,首先对二元确知信号检测判决系统中相干相移键控系统(CPSK),相干频移键控系统(CFSK),和相干启闭键控系统(COOK)的检测性能进行分析。其次,在此基础上推广到对多元(M元信号检测)确知信号检测性能进行分析。最后,经过仿真,根据最大输出信噪比准则,采用白化滤波器组将色噪声滤除有色部分后通过检测判决系统,获取在色噪声背景下最佳的线性滤波器组(Linear Filter Bank,LFB)(相关接收机)的检测性能。     

Data transmission over GSM voice channel using digital modulation technique based on autoregressive modeling of speech production

This paper presents a novel digital data modulation and demodulation algorithm ARDMA based on the principles of autoregressive modeling (AR) of speech production. In the first step a sustained voiced speech signal characteristics are analyzed using autoregressive modeling principle and then the two sets of linear prediction (LPC) coefficients are obtained and converted to linear spectrum frequencies (LSF). The input binary data stream drives the selection mechanism of LSF coefficients which are then applied as filter coefficients of the modulation signal synthesis filter. This filter is excited with specially designed excitation signal which corresponds to the basic characteristics of typical excitation signal of human vocal tract. Finally, a speech-alike modulation signal is produced. This modulation signal is then sent through the voice channel of the GSM system. The demodulator analyzes the incoming modulation signal using autoregressive modeling. The most likely LSF vector which modulated the particular symbol was determined by the demodulation process and converted to the respective string of binary data. The performance of proposed modulation scheme was compared to the regular frequency shift keying method (FSK). The performance improvement of ARDMA against FSK is observed at higher bit-rates in the case of three compared GSM speech coders.     

一种用于DQPSK卫星通信系统中窄带干扰检测的新方法

以频分多路(FDMA)差分四相移键控(DQPSK)调制方式的数字卫星通信中抗窄带干扰项目为背景，在建立干扰的数学模型基础上，提出了一种变换域方法——快速傅立叶(FFT)变换方法进行干扰检测。这种方法不是采用一个统一的门限值，而是用类似于信号功率谱形状的门限来检测信道中干扰的载波频率、功率、带宽，并根据这些检测值更加精确地设计陷波器的参数。仿真表明当输入信号的信干比为-42dB时，用类似于信号功率谱形状的门限设计的陷波器输出端的信干比为-18dB，而用传统门限设计的陷波器输出端的信干比为-24dB。新方法比传统方法输出信干比提高了6dB。     

Statistical demodulators for frequency shift keying with fast frequency hopping

This work is devoted to the problems of information transmission with frequency shift keying and fast frequency hopping in special channels where the signal/noise ratio is low, and a high energy interfering signal is present. We propose a demodulation algorithm that is significantly more stable to the influence of a powerful interfering signal as compared to other known algorithms. Under these conditions, we show a statistical criterion that lets one significantly reduce error probability on the demodulator’s output. For the chosen criterion we prove several lemmas that let us speed up the demodulation algorithm. Computer modeling results show that the proposed demodulation algorithm has better correcting ability under a powerful interfering signal than previously known ones.     

一种基于自适应陷波器的科氏流量计频率解算新方法

为提高科氏流量计信号的初始收敛速度和频率跟踪精度,提出了一种频率解算的新方法:首先采用基于burg算法实现的格型IIR自适应陷波器对信号滤波,并短时间跟踪信号频率,其接近收敛时基于简化梯度算法实现的格型自适应陷波器开始并行工作,待简化梯度算法实现的格型自适应陷波器收敛后,前者停止工作,简化梯度算法实现的格型自适应陷波器持续工作直至结束。仿真及实验结果表明,本方法可以获得更快的收敛速度、更高的频率跟踪精度。     

Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform

The information extraction capability of two widely used signal processing tools, Hilbert Transform (HT) and Wavelet Transform (WT), is investigated to develop a multi-class fault diagnosis scheme for induction motor using radial vibration signals. The vibration signals are associated with unique predominant frequency components and instantaneous amplitudes depending on the motor condition. Using good systematic and analytical approach this fault frequencies can be identified. However, some faults either electrical or mechanical in nature are associated with same or similar vibration frequencies leading to erroneous conclusions. Genetic Algorithm (GA) is proposed and used successfully to find the most relevant fault frequencies in radial (vertical) frame vibration signal which can be used to diagnose the induction motor faults very effectively even in the presence of noise. The information obtained by Continuous Wavelet Transform (CWT) was found to be highly redundant compared to HT and thus by selecting the most relevant features using GA, the fault classification accuracy has considerably improved especially for CWT. Almost similar fault frequencies were found using CWT + GA and HT + GA for radial vibration signal.     

一种采用B样条插值的改进能量算子在电梯电机轴承故障诊断中的应用

针对传统Teager能量解调算子方法对电梯运行系统中存在的强背景噪声较为敏感的不足,提出了一种改进的能量解调算子方法;采用了B样条技术与传统Teager能量算子方法进行结合,其中建立的B样条曲线对信号进行插值起到滤波作用;然后再利用Teager能量算子对滤波信号进行转换;最后利用傅里叶变换得到转换信号的频谱图从而揭示故障特征;所提出的基于B样条插值的能量解调方法不仅保留了传统能量解调算法的优点,如较高的解调精度和优秀的时间分辨率等,并且可以在强噪声背景下提取出微弱轴承故障特征;经实验验证实现了提高强背景噪声下的轴承故障检测的性能,能够在故障退化的早期检测故障,满足了实际工况下故障诊断上的应用。     

用于消除谐波干扰的Ｖ型滤波器的零极点配置设计方法

针对某设备振动模态测试中,市电对测试信息产生的５０Ｈz工频及其谐波干扰,设计了阻带带宽极狭窄（小于１Ｈz）的Ｖ型数字带虎波器,并完成了相应的数字滤波,Ｖ型数字滤波器物理意义清晰,设计和实现方法简单灵活,效果大大优于某些消除谐波干扰的算法,本文介绍了该Ｖ型滤波器的设计及实现方法。     

异构复杂信息网络下的异常数据检测算法

异构复杂信息网络承载着不同的协议和网络信道,并通过云储存实现资源调度,由此产生的异常数据会给网络信息空间带来安全威胁和存储开销,所以需要进行异常数据准确检测。传统的检测算法采用简化梯度算法进行异常数据检测,不能有效去除多个已知干扰频率成分的异常数据,检测性能不好。提出一种基于自适应陷波级联模型的异常数据检测算法。构建异构复杂信息网络系统模型,采用固有模态分解把异常数据信号解析模型分解为多个窄带信号,设计二阶格型陷波器结构,用多个固定陷波器级联抑制干扰成份,采用匹配投影法寻求优化特征解,找出所有匹配的特征点对,从而实现异常数据检测的改进。仿真实验表明,采用该算法进行异常数据检测时,信号幅值大于干扰噪声数据幅值;该算法提高了检测性能,具有较好的抗干扰性能。     

弱激励石英晶体参数检测的自适应陷波法

微弱激励下的石英晶体其输出信号的频率、幅度和相位输出变化都非常大。本文采用π网络零相位检测法测量石英晶体的串联谐振频率,针对微弱信号的相位检测采用自适应陷波滤出期望信号,然后对陷波后信号进行带宽压缩提高信噪比,从而检测出π网络零相位点,估计出石英晶体的谐振频率。文中研制了基于数字信号处理器的检测系统,实验表明系统在信号功率小于-60dBm情况下,相位检测精度达到2PPM。     

GNSS accuracy enhancement based on pseudo range error estimation in an urban propagation environment

For new ITS applications, positioning solutions will require to be more accurate and available. The most common technique used today is composed of a GPS receiver, sometimes aided by other sensors. GPS, and GNSS in general, suffer from masking effects and propagation disturbances in urban areas that cause biases on pseudo range measurements. Mitigation solutions sometimes propose to detect and exclude outliers but in land transportation applications, such a decision reduces dramatically the service availability and thus, the interest of satellite-based solutions. In order to optimize the use the satellites received, we propose a new positioning algorithm based on signals only with pseudo range error modeling in association with an adapted filtering process. The model and the filter have been validated with simulation data performed along an urban bus line and have shown that both positioning error and availability can be improved. Along the trajectory tested, the mean accuracy has been reduced from 5.3 m with a classical filter to 2.6 m with our algorithm with 89% of the points more accurate than 5 m instead of 64% before.     

基于循环谱的猝发信号解调

针对传统解调方法应用于猝发信号解调的缺点,提出了一种运用循环谱解调猝发信号算法,采用循环谱快速估计猝发信号的载波频率,根据载波频率进行猝发信号的解调,对以BPSK和QPSK调制方式的猝发信号进行了仿真。仿真结果显示：在满足一定约束条件下,该方法可以快速地捕获猝发信号的载波频率,从而实现了在较短的时间内解调出猝发信号,并且采用此算法对猝发信号进行解调的结果和理论分析一致。     

基于重叠短汉宁窗DTFT算法的科氏流量计信号处理方法*

本文提出了一种基于重叠短汉宁窗DTFT算法的科氏流量计信号处理方法：首先采用新式自适应陷波器对科氏流量计时变信号进行滤波以求其频率,然后采用重叠短汉宁窗DTFT算法实时计算两路信号之间的相位差,再根据频率和相位差求得时间差,最终测得质量流量。给出的仿真和实测结果表明本文方法是实用有效的。     

Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time–frequency information

In this study, a novel digital modulation classification model has been proposed for automatically recognizing six different modulation types including amplitude shift keying (ASK), frequency shift keying (FSK), phase-shift keying (PSK), quadrate amplitude shift keying (QASK), quadrate frequency shift keying (QFSK), and quadrate phase-shift keying (QPSK). The determination of modulation type is significant in military communication, satellite communication systems, and submarine communication. To classify the modulation types, we have proposed a two-stage hybrid method combining short-time Fourier transform (STFT) and convolutional neural network (CNN). In the first stage, as the data source, the time–frequency information from these modulation signals have been extracted with STFT. This information has been obtained as 2D images to feed the input of the CNN deep learning method. In the second stage, the obtained 2D time–frequency information has been given to the input of the CNN algorithm to classify the modulation types. In this work, noises at various SNR values from 0 dB to 25 dB were created and added to the modulated signals. Even in the presence of noise, the proposed hybrid deep learning model achieved excellent results in the noised-modulation signals.     

Computerized analysis of fetal heart rate signals as the predictor of neonatal acidemia

Cardiotocography is the primary method for biophysical assessment of fetal state, which is mainly based on the recording and analysis of fetal heart rate (FHR) signal. Computerized systems for fetal monitoring provide a quantitative analysis of FHR signals, however the effective methods of qualitative assessment that could support the process of medical diagnosis are still needed. The measurements of hydronium ions concentration (pH) in neonatal cord blood are an objective indicator of the fetal outcome. Improper pH level is a symptom of acidemia being the result of fetal hypoxia. The paper proposes a two-step analysis of fetal heart rate recordings that allows for effective prediction of the acidemia risk. The first step consists in fuzzy classification of FHR signals. Fuzzy inference corresponds to the clinical interpretation of signals based on the FIGO guidelines. The goal of inference is to eliminate recordings indicating the fetal wellbeing from the further classification process. In the second step, the remained recordings are nonlinearly classified using multilayer perceptron and Lagrangian Support Vector Machines (LSVM). The proposed procedures are evaluated using data collected with computerized fetal surveillance system. The assessment performance is evaluated with the number of correct classifications (CC) and quality index (QI) defined as the geometric mean of sensitivity and specificity. The highest CC = 92.0% and QI = 88.2% were achieved for the Weighted Fuzzy Scoring System combined with the LSVM algorithm. The obtained results confirm the efficacy of the proposed methods of computerized analysis of FHR signals in the evaluation of the risk of neonatal acidemia.     

Evaluating the use of ECG signal in low frequencies as a biometry

Traditional strategies, such as fingerprinting and face recognition, are becoming more and more fraud susceptible. As a consequence, new and more fraud proof biometrics modalities have been considered, one of them being the heartbeat pattern acquired by an electrocardiogram (ECG). While methods for subject identification based on ECG signal work with signals sampled in high frequencies (>100 Hz), the main goal of this work is to evaluate the use of ECG signal in low frequencies for such aim. In this work, the ECG signal is sampled in low frequencies (30 Hz and 60 Hz) and represented by four feature extraction methods available in the literature, which are then feed to a Support Vector Machines (SVM) classifier to perform the identification. In addition, a classification approach based on majority voting using multiple samples per subject is employed and compared to the traditional classification based on the presentation of single samples per subject each time. Considering a database composed of 193 subjects, results show identification accuracies higher than 95% and near to optimality (i.e., 100%) when the ECG signal is sampled in 30 Hz and 60 Hz, respectively, being the last one very close to the ones obtained when the signal is sampled in 360 Hz (the maximum frequency existing in our database). We also evaluate the impact of: (1) the number of training and testing samples for learning and identification, respectively; (2) the scalability of the biometry (i.e., increment on the number of subjects); and (3) the use of multiple samples for person identification.     

轴承早期故障特征提取方法研究

针对滚动轴承早期故障信号被背景噪声淹没、故障特征不明显的问题,提出一种基于小波包分解和互补集合经验模态分解(CEEMD)的轴承早期故障信号特征提取方法.利用Matlab软件对采集到的轴承振动信号进行快速谱峭度分析,根据峭度最大化原则确定带通滤波器的中心频率和带宽,设计带通滤波器;对经过带通滤波器滤波后的信号进行小波包分解和CEEMD分解,根据峭度、相关系数筛选出有效本征模态函数(IMF)分量;利用IMF分量重构小波包信号,对重构小波包信号进行包络谱分析,提取轴承早期故障信号特征频率.该方法通过谱峭度分析降低背景噪声干扰,通过小波包分解增强故障冲击信号,并将CEEMD与小波包分解相结合,解决经典EMD分解存在的模态混叠、无效分量问题.仿真结果表明,相较于传统包络解调算法,重构后信号的背景噪声得到抑制,故障特征分量突出,验证了所提方法的可行性和有效性.     

基于全通滤波器的IIR陷波器抑制信号中的周期性干扰

从滤波器的系数敏感度角度 ,提出一种基于全通滤波器的 IIR陷波器来抑制信号中的周期性干扰。该滤波器是由全通滤波器的级联构成的 ,因为全通滤波器传递函数的分子和分母多项式之间的镜像对称关系 ,该滤波器可以用高效的格型运算结构来实现 ,并且具有很低的系数敏感度 ,使得系数量化后的陷波频点偏移量最小。本文详细论述了该陷波器的设计方法 ,并深入分析了其在低频段的系数敏感度问题。通过实际数据验证了这种滤波器能够有效的抑制电力线通信信号中的谐波干扰。     

Multichannel direct sequence spectrum signaling using code phase shift keying

In this paper, we have designed and realized multichannel direct sequence spread spectrum (DSSS) system using code phase shift keying (CPSK). In the transmitter, taking each bit from each channel, data word is made as a symbol for selecting PN sequence, which is modulated with frequency of 100 MHz as DSSS signals. At the receiver, the correlator, integrator and decoder are used for separation of the signal of respective channel after demodulation. Oscilloscope traces show that the transmitted signals are matched with the simulated signals at the receiver. The bit error rate (BER) variation with jamming signal is estimated by our proposed simulation model, matching well with experimental values of BER measured by BER meter.