Wavelet threshold method of resolving noise interference in periodic short-impulse signals chaotic detection

The chaotic oscillator has already been considered as a powerful method to detect weak signals, even weak signals accompanied with noises. However, many examples, analyses and simulations indicate that chaotic oscillator detection system cannot guarantee the immunity to noises （even white noise）. In fact the randomness of noises has a serious or even a destructive effect on the detection results in many cases. To solve this problem, we present a new detecting method based on wavelet threshold processing that can detect the chaotic weak signal accompanied with noise. All theoretical analyses and simulation experiments indicate that the new method reduces the noise interferences to detection significantly, thereby making the corresponding chaotic oscillator that detects the weak signals accompanied with noises more stable and reliable.

Hypothesis testing analysis and unknown parameter estimation of GPS signal detection

Hypothesis testing analysis and unknown parameter estimation of both the intermediate frequency(IF) and baseband GPS signal detection are given by using the generalized likelihood ratio test(GLRT) approach,applying the model of GPS signal in white Gaussian noise,It is proved that the test statistic follows central or noncentral F distribution,It is also pointed out that the test statistic is nearly identical to central or noncentral chi-squared distribution because the processing samples are large enough to be considered as infinite in GPS acquisition problem.It is also proved that the probability of false alarm,the probability of detection and the threshold are affected largely when the hypothesis testing refers to the full pseudorandom noise(PRN) code phase and Doppler frequency search space cells instead of each individual cell.The performance of the test statistic is also given with combining the noncoherent integration.

A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning

The localized faults of rolling bearings can be diagnosed by its vibration impulsive signals.However,it is always a challenge to extract the impulsive feature under background noise and non-stationary conditions.This paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary learning.To overcome the effects harmonic and noise components,we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission path.To address the shortcomings of conventional sparse representation under the changeable operation environment,we propose an approach that combines K-clustering with singular value decomposition(K-SVD)and split-Bregman to extract impulsive components precisely.Via experiments on synthetic signals and real run-to-failure signals,the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed approach.Meanwhile,a comparison with the state-of-the-art methods is illustrated,which shows that the proposed approach can provide more accurate detected impulsive signals.

QRS waves detection algorithm based on positive-negative adaptive threshold method

In order to accurately detect the occasional negative R waves in electrocardiography （ECG） signals, the positive-negative adaptive threshold method is adopted to determine the positive R waves and the negative R waves, according to difference characteristics of ECG signals. The Q and S waves can then be accurately positioned based on the basic characteristics of QRS waves. Finally, the algorithm simulation is made based on the signals from MIT-BIH database with MATLAB. The ex- perimental results show that the algorithm can improve the detection accuracy rate to 99. 91% and o- vercome the problem of larger computation load for wavelet transform and other methods, so the al- gorithm is suitable for real-time detection.

Threshold Determination and Analysis of Laser Pulse Range Finder

Under different conditions, the highest detection probability should be acquired while receiving laser echo during laser pulse range finding. The threshold voltage of the signal detection can be set corresponding to different conditions by using resistor network. As a feedback loop, automatic noise threshold circuit could change the threshold voltage following the noise level. The threshold can track the noise closely, rapidly and accurately by adopting this combination. Therefore, the receiving capability of laser echo receiving system will be maximized, and it can detect weaker laser pulse from noise.

A ROBUST COLOR EDGE DETECTION ALGORITHM BASED ON THE QUATERNION HARDY FILTER

This paper presents a robust filter called the quaternion Hardy filter(QHF)for color image edge detection.The QHF can be capable of color edge feature enhancement and noise resistance.QHF can be used flexibly by selecting suitable parameters to handle different levels of noise.In particular,the quaternion analytic signal,which is an effective tool in color image processing,can also be produced by quaternion Hardy filtering with specific parameters.Based on the QHF and the improved Di Zenzo gradient operator,a novel color edge detection algorithm is proposed;importantly,it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique.From the experimental results,we conclude that the minimum PSNR improvement rate is 2.3%and the minimum SSIM improvement rate is 30.2%on the CSEE database.The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.

PMF+双门限Tong检测的北斗B1I信号捕获算法

针对传统Tong检测算法检测时间长、漏警概率大的问题,提出将PMF+双门限Tong检测算法应用于北斗B1I信号的捕获中。首先,将射频前端模块处理得到的中频信号下变频至基带,对基带信号进行降速等预处理;然后,利用部分匹配滤波(PMF)算法计算相关值,通过PMF算法将数据信号分成几个均等片段,对每一段信号都分别计算相关值,会出现三组具有同相位的相关结果;最后,利用双门限Tong检测算法对PMF算法计算得到的相关结果检测,达到捕获B1I信号的目的。仿真结果表明,利用该算法捕获北斗B1I信号,有效提高了捕获时的检测概率并缩短了检测时间,提高了捕获B1I信号的速度。

直埋热水供热管道泄漏定位检测实验研究

选用傅里叶滤波法和小波阈值法进行了管道泄漏信号降噪和泄漏定位的对比研究。在DN300管路的实验直埋热水供热系统上进行了泄漏检测实验,采用小管径支管和调节阀模拟泄漏点、加速度传感器测量声波振动信号,依次选用2种滤波方法对直埋热水供热管道泄漏的声波振动信号进行数据降噪处理,讨论了泄漏信号降噪前后的信号频域特征和定位精度。在北京市某小区直埋供热管道进行了声波降噪泄漏检测工程验证,定位偏差不超过1.0 m。研究表明,傅里叶滤波法和小波阈值法对直埋热水供热管道泄漏定位的声波振动信号降噪处理效果显著,小波阈值法保持了全频段的信号特征,可获得更高的定位精度。

基于门限的5G空间调制消息传递检测算法

针对经典消息传递检测算法中存在复杂度高与性能低的问题,文中基于门限和消息传递机制,提出一种适用于5G通信的空间调制信息检测算法。该算法通过引入组合数学表示方法,在广义空间调制系统完成大规模天线组合的表示,进而构建基本的分层消息传递检测模型。在此基础上,利用设定门限的方法快速地判定接收信号的欧氏距离,以减少消息传递检测算法执行无效的迭代,从而实现具有较低复杂度的消息传递检测算法。理论分析及实验仿真结果表明,与分层消息传递检测算法相比,基于门限的消息传递检测算法具有更高的检测性能与较低的计算复杂度。

强噪声中检测微弱目标信号特征的量子信号处理算法

随着噪声功率的增强,微弱目标信号的特征受噪声污染变得模糊且难以区分,导致微弱信号检测算法失效,提出一种可以保护目标信号特征的量子信号处理方法——局域半经典信号分析算法。详细介绍了算法实现量子化的原理和在量子域中保护目标信号特征的性质;给出算法步骤以及重要参数的计算方式;将所提算法与奇异值分解、小波阈值降噪算法结合进行了仿真分析和实验验证。结果表明,所提算法保护目标信号特征的能力可以帮助降噪算法检测极低信噪比的微弱信号,与其他方法结合可极大改善信噪比,准确提取信噪比为-30 dB的微弱目标信号,算法性能优越。

非平稳强噪声环境中的音频信号端点检测系统

为提高音频信号端点识别能力,设计一种非平稳强噪声环境中的音频信号端点检测系统。构建音频信号端点检测硬件单元,利用预处理单元对音频信号进行预加重、分帧以及加窗处理后,端点检测单元在提取处理音频信号的MFCC倒谱距离特征、频带方差特征的基础上,依据动态阈值估计策略确定恰当阈值;通过双特征参数双门限法来实现对音频信号起止点的确定以及语音帧和非语音帧的分离;利用包络确定延时单元,防止噪声段被错误识别为语音段,避免出现拖尾太长问题。实验结果表明,所设计系统可实现非平稳强噪声环境音频信号端点检测,检测误差满足设定要求。

改进停止阈值准则的振动信号降噪方法研究

在使用压缩感知理论解决振动信号的降噪问题时,信号重构阶段使用最多的是正交匹配追踪算法,如果该算法的迭代停止阈值选取不当,迭代误差的循环将会导致重构误差增大,降噪性能会下降。提出一种基于改进停止阈值准则的降噪方法,利用离散余弦变换得到完备字典矩阵,对振动信号进行稀疏表示,得到稀疏系数向量;然后用正态分布检验稀疏系数是否服从正态分布,利用3σ准则计算得到停止阈值;最后在求解最小二乘解的步骤之后添加筛选判断条件与迭代停止阈值。仿真定义信号和实测振动信号的降噪分析结果表明:此改进方法可将信噪比(SNR)为-1.0257 dB强噪声背景信号的信噪比提升到2.0549 dB。此方法在信噪比、均方差根等降噪性能指标上均优于SP、OMP、SWOMP三种降噪方法。

多定频干扰下准随机跳频信号检测仿真

多定频干扰环境的复杂性会对准随机跳频信号产生影响,导致跳频信号检测效果不佳。为了提升检测性能,提出多定频干扰下准随机跳频信号检测方法。对多定频干扰下的准随机跳频信号开展分析,获取跳频信号,并对信号实行小波阈值去噪处理;根据去噪处理结果,对准随机跳频信号开展傅里叶变换,求解跳频信号的局部自适应阈值,以此消除定频干扰,完成跳频信号提取;检测提取信号在时域的驻留连续性,区分跳频信号及可疑目标信号,实现多定频干扰下准随机跳频信号的检测。实验结果表明,通过开展信噪比测试、检测误差测试,验证了该方法的检测效果较好,说明其具有实用性和有效性。

复值Hopfield神经网络的信号盲检测一步计算电路

信号盲检测在大规模通信网络中具有重要的意义并得到了广泛的应用,如何快速得到信号盲检测结果是新一代实时通信网络的迫切需求。为此,该文从模拟电路的角度设计了一种能加速信号盲检测的复值Hopfield神经网络(CHNN)电路,该电路可一步完成大规模并行计算,提高信号盲检测速度,同时该电路可以通过调整忆阻器的电导和输入电压来实现可编程功能。Pspice仿真结果表明,该电路的计算精度可达99%以上,运行时间比Matlab软件仿真快3个数量级,此外,该电路具有良好的鲁棒性,即使在20%的噪声干扰下,仍能保持99%以上的计算精度。

深度残差网络下的电缆隧道低压故障检测

针对数据梯度消失导致电缆隧道低压故障检测结果精度不高的问题,提出了一种基于深度残差网络的电缆隧道低压故障检测方法。该方法对电缆隧道低压回路电流进行分析,建立深度残差网络,通过初始化学习得到线路电流故障特征值,并在权重层中引入激活函数,结合传递梯度进行特征重构,有效避免了梯度消失问题,输出信号特征,通过过零率判别得出故障检测结果。实验结果表明:应用所提方法得出的故障检测结果表现出的高频系数衰减比值图像呈现均值变化且较为密集,检测结果精度较高,满足了电缆隧道低压回路的现实运维需求。

逆变器高频谐波扰动电子信号自动检测方法

研究逆变器高频谐波扰动电子信号自动检测方法。构建逆变器的电子信号采样函数,获取电子信号采样结果。利用改进后的阈值函数对电子信号进行小波去噪处理,通过混合字典对去噪后的信号进行稀疏分解,确定电子信号高频谐波扰动特征。利用BFGS校正拟牛顿算法优化BP神经网络的梯度和参数,将电子信号高频谐波扰动特征输入优化BP神经网络中,得到逆变器高频谐波扰动电子信号自动检测结果。实验结果表明,所提方法能精准获取逆变器电子信号,对高频谐波扰动电子信号的检测误差小,效果好。

X射线数字成像图像质量影响因素探讨

X射线数字成像检测系统是在射线透射工件的层面的同时,通过计算机及数字处理软件直接观察工件内部情况。通过X射线数字成像检测系统采集不同参数下的图像,对管电压、管电流、增益、单帧曝光时间及积分次数与图像灰度值和图像信噪比间的关系进行了统计分析,结果表明增益与图像灰度值基本上成反相关关系,增益几乎对图像信噪比无影响。该实验为图像灰度值和图像信噪比的影响因素的研究提供了有力的数据依据。

基于小波熵的微弱信号检测方法研究

在科学技术研究领域中,经常会遇到非平稳、低能量、瞬时变化的微弱信号检测问题,然而,微弱的有用信号往往被环境噪声所湮没,最大程度地提取有用信息一直是弱信号检测中的一个难题。尤其对短时低能量的瞬变信号,采用传统信号处理方法提取其位置信息难以奏效。小波分析的方法为弱信号检测技术开辟了一条新途径,但小波变换对弱信号进行特征提取的关键在于确定小波系数的阈值。为此,在软阈值基础上引入反映信号能量分布特性的小波熵概念,利用信号在不同分解尺度上具有不同的小波熵,能够自适应地确定高频系数分量的阈值。仿真分析表明,基于小波熵分析的方法能够在强噪声环境中对微弱信号准确定位,实现低能量的瞬变信号有效提取。

生物电信号峰值测量方法与实现

生物电信号由于其发生机理和所处的生物体的电特性比较复杂、多变，故在处理这样的信号时，常规方法和电路往往难以实现不失真地撷取、放大和精确定量。介绍了一种生物电信号峰值测量方法，详细阐述了依据该方法设计的实用电路的结构、测试结果、误差分析及改进措施，并指出该测量方法具有构思巧妙，原理简单，易实现等优点，适用于大部分需要测量生物电信号峰值的系统中。

低压系统串联故障电弧在线检测方法

故障电弧是引发电气火灾的主要原因,有效可靠地检测出故障电弧是预防电气火灾的迫切要求。本文首先基于居民用电系统搭建了模拟串联故障电弧的实验平台,而后设计了以常见家用电器为负载的实验方案并采集到不同条件下的故障电弧信号。基于电弧电流的特性分析,提出了一种故障电弧在线检测方法。该方法在线路电流相邻周期相减的基础上进行小波阈值去噪和归一化,利用周期幅值作为电弧的特征量,将特征量与参考值进行比较来判断故障电弧的发生。通过对单一负载、组合负载、启动过程、正常工作以及不同电压下串联故障电弧实验数据进行分析,得到了不同负载下电弧特征量的公共阈值。与其他方法的比较结果表明所提方法能够有效检测故障电弧的发生。