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.

Research on Estimation of Time Delay Difference in Passive Locating for Impulse Signal

Analyzed the relation between time delay difference and time delay estimation errors, based on the principles of three-point locating, a reformed threshold method for time delay difference estimation of impulse signals, called as amendment estimation for short, is developed by introducing channel equalization technique to its conventional version, named as direct estimation in this paper, to improve the estimation stability. After inherent relationship between time delay and phase shift of signals is analyzed, an integer period error compensation method utilized the diversities of both contribution share and contribution mode of concerned estimates is proposed under the condition of high precision phase lag estimation. Finally, a cooperative multi-threshold estimation method composed of amendment and direct estimations to process impulse signals with three thresholds is established. In sea trials data tests of passive locating, this method improves the estimation precision of time delay difference efficiently. The experiments verify the theoretical predictions.

Novel Sampling and Reconstruction Method for Non-Bandlimited Impulse Signals

To sample non-bandlimited impulse signals, an extremely high-sampling rate analog-todigital converters （ADC） is required. Such an ADC is very difficult to be implemented with present semiconductor technology. In this paper, a novel sampling and reconstruction method for impulse signals is proposed. The required sampling rate of the proposed method is close to the signal innovation rate, which is much lower than the Nyquist rate in conventional Shannon sampling theory. Analysis and simulation results show that the proposed method can achieve very good reconstruction performance in the presence of noise.

Characteristics analysis of muzzle impulse noise based on wavelet energy spectrum

Because muzzle impulse noise could cause damage to or have an intluence on the operator, tiae ettecnve protecnve measures should be taken. Therefore, correct analysis of impulse noise characteristics is very significant. Considering the shortcomings of fast Fourier transform method （FFT） in analysis of muzzle impulse noise frequency characteristics, wavelet energy spectrum method is put forward. Based on specific experiment data, the frequency characteristics and spectral energy dis tribution can be obtained. The experiment results show that wavelet energy spectrum method is applicable in muzzle impulse noise characteristic analysis.

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.

A Method for Measuring LC Resonance Frequency by Impulse Response

LC circuit resonance frequency measurement often requires the use of professional analysis instruments such as LCR meters,vector network analyzers,but currently such instruments on the market are expensive,and it is difficult for non-professional institute personnel to access.Here comes unnecessary trouble.In view of this situation,a test method for measuring the resonance frequency using only a digital storage oscilloscope is proposed.Using the impulse signal to obtain the system response,the response waveform period can be observed through the oscilloscope.

脉冲信号的奇异性分布熵特征分析

为了对低信噪比复杂环境下脉冲信号的奇异性差异进行有效的分析和标定,提出了一种基于模极大值理论的奇异性分布熵特征分析模型。首先对脉冲信号进行归一化并进行小波变换,计算各尺度下模极大值及其特定分布,可以体现具有奇异性差异的模极大值曲线族。为定量描述这种差异性,用熵值表达构成模极大值曲线族的模极大值点分布,并构建能有效分析脉冲信号奇异性差异的奇异性分布熵特征模型。该模型能对低噪比下信号的奇异性差异进行刻画。实验结果表明,在信噪比为-6 dB的环境下对典型的直升机脉冲信号(桨/涡干扰信号和高速脉冲信号)进行分析,能够得到89.25%和87.63%的正确率。

脉冲噪声下基于CNN-FRFT的线性调频信号参数估计方法

由于脉冲噪声破坏了线性调频(LFM)信号的分数谱特征,使得基于分数谱特征的参数估计方法无法有效估计参数。针对这个问题,提出一种脉冲噪声环境下基于CNN-FRFT的LFM信号参数估计方法。首先,利用α稳定分布拟合随机脉冲噪声,构建加性含噪信号,输入卷积神经网络(CNN)进行训练和测试;其次,利用训练好的CNN模型对信号进行去噪,并验证模型的去噪能力和泛化能力;最后,利用分数阶傅里叶变换(FRFT)建立去噪信号的分数谱,通过峰值点位置来估计LFM信号的参数。实验结果表明,相比于传统的基于非线性函数的方法,该方法在强脉冲噪声环境下具有更好的精度和噪声鲁棒性,CNN的应用使其具有更强的泛化能力,在实测脉冲噪声下仍可以准确估计参数。

脉冲噪声下基于Sigmoid的LFM信号参数估计

由于脉冲噪声具有的短时大幅值特性,使得基于高斯假设的信号参数估计方法无法在脉冲噪声环境下有效估计参数。针对此问题,利用α稳定分布模拟随机脉冲噪声,提出了一种基于Sigmoid-CFRFT的LFM信号参数估计方法。首先,建立了一种自适应Sigmoid函数,证明了信号经过此非线性变换后,信号的2阶矩由无界变为有界,且信号的相位信息保持不变。其次,将变换后的信号进行离散时间CFRFT,建立了数学优化模型,并使用水循环算法搜索最优值点。最后,利用了非标准SαS分布噪声的修正方法,分析了标准和非标准分布下参数估计的性能。仿真结果说明,所提方法不仅可以有效抑制脉冲噪声对LFM信号分数谱特征的影响,而且能够实现低信噪比信号参数的高精度估计。相比于现有的基于非线性变换的参数估计方法,本文方法具有更好的精度,稳定性和噪声鲁棒性。

大跨径钢桁架双层拱桥模态阻尼比分析方法

为有效揭示结构整体刚度的变化,解决当前模态阻尼识别准确性方面存在的突出问题,特别是在运营中的复杂桥梁结构,难以准确分离多种干扰因素,创新研究了复杂桥梁的模态阻尼比识别方法,只需有限数量的加速度传感器,即可精确且高效地分析复杂桥梁的模态阻尼比。首先,采用变分模态解耦方法,将实测的非线性振动信号准确分解为单一频率模式;其次,对这些分解后的信号应用希尔伯特变换和脉冲响应函数进行处理,从而建立模态阻尼比的求解策略;然后,选取服役中的公轨同层大跨径钢桁架双层拱桥作为实证研究对象,收集其主梁的加速度响应信号;再次,通过变分模态解耦与脉冲响应函数方法,成功分析信号特性,获取该结构的频率及阻尼比;最后,将这些频率数据与有限元模型分析结果进行比对,证实方法的准确性和可靠性。

铁路信号机房外部冲击电流监测原理及软件分析策略

针对铁路信号机房应用场景下常见的外部冲击电流问题,从基本监测原理、软件分析策略两方面提出具体解决方案,基于该方案可实现对多频段冲击电流的综合采集以及冲击电流详情自动化分析,实现对设备管理单位的指导性建议,提升铁路信号机房运行的安全性。

Signal processing circuit of laser gyro based on FPGA and DSP

This is a paper about laser gyro sign a l processing circuit which is designed based on field-programmable gate array(FPGA) and digital signal processor(DSP).Through a pre-amplifier circuit,FPGA and DSP,a weak current signal is converted and transferred,then sent to the computer to display the final results.Through the laser gyro performance te sting,the obtained results coincide with those of the existing methods.Thus th e d esigned circuit realizes the function of laser gyro signal processing.

一种NLF-对数小波阈值的脉冲干扰降噪方法

在脉冲噪声背景下,传统信号降噪方法性能退化,无法去除强脉冲噪声,而且会出现计算复杂度增加的问题.本文提出一种基于非线性滤波(Nonlinear Filtering, NLF)和小波阈值去噪的联合算法,该方法利用非线性滤波算法抑制强脉冲噪声点干扰,通过改进的对数小波阈值函数去噪方法进行全局处理,快速而且高质量的完成对信号降噪.实验结果表明,本文所提算法对脉冲噪声背景下的多种信号类型都具有可行性,能够获得更高的峰值信噪比,具有很好的降噪效果.

基于时间模式信号处理的三阶FIR滤波器实现

文中提出了一种基于时间模式信号处理电路的三阶低通有限脉冲响应(FIR)滤波器的实现。滤波器拓扑由一组新颖的构建块组成,这些构建块在时间模式下执行必要的功能,包括z-1运算、时间加法和时间乘法。设计的时域三阶FIR滤波器的输出与采样频率同步,且该滤波器的拓扑结构是模块化的,因此只需增加延迟和乘法器的数量就可以创建高阶滤波器。滤波器是在28 nm三星全耗尽绝缘体上硅FD-SOI工艺中设计的,电源电压为1 V,采样频率为5 MHz,仿真结果验证了理论分析的正确性,FIR滤波器在50 kHz的输入频率下实现了38.6 dB的信噪加失真比(SNDR),功耗约为200μW。

一种冲击噪声下相干分布源多峰DOA估计方法

针对现有相干分布源波达方向(Direction Of Arrival,DOA)估计方法计算量大、抗冲击噪声能力弱和不能有效去相干等难题,本文提出了一种冲击噪声下相干分布源多峰DOA估计方法,并推导了冲击噪声下相干分布源DOA估计的克拉美罗界.为了实现冲击噪声下相干分布源DOA估计,采用加权范数协方差抑制冲击噪声,进而首次推导出多峰加权信号子空间拟合方程,并设计了一种多峰量子秃鹰算法快速无量化误差求解.仿真结果表明,所提方法在冲击噪声下能够以较小的快拍数实现相干分布源DOA估计,且无需额外的解相干操作即可有效去相干.与一些已有的高精度DOA估计方法相比,所提方法仿真时间明显缩短,且具有更高的估计精度和估计成功概率,突破了已有相干分布源DOA估计方法的应用局限,可推广应用于其他复杂的DOA估计问题中.

冲击噪声下基于演化长短时记忆神经网络的调制信号识别

为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolution neural network,CNN)去噪模型对数据集去噪;结合量子计算机制和旗鱼优化器(sailfish optimizer,SFO)设计了量子旗鱼算法(quantum sailfish algorithm,QSFA)去演化LSTM神经网络以获得最优的超参数;使用演化长短时记忆神经网络作为分类器进行自动调制信号识别。仿真结果表明,采用所设计的CNN去噪和演化长短时记忆神经网络模型,识别准确率有了大幅度的提高。量子旗鱼算法演化LSTM神经网络模型降低了传统LSTM神经网络容易陷于局部极小值或者过拟合的概率,当混合信噪比为0 dB,所提方法对11种调制信号的平均识别准确率达到90%以上。

基于PCIe的高精度低杂散信号源设计

为了在动态压力测试系统中提供1种精度高、便携式与可靠性强的信号源以完成校准测试工作,通过采用现场可编程门阵列(FPGA)芯片作为控制核心,设计了1种高精度、低杂散的信号源。基于直接数字式频率合成器(DDS)基本原理,利用高速串行计算机扩展总线(PCIe)标准发送上位机指令及大量波形数据,通过16位的4通道模数转换芯片DAC8544输出各类型信号。采用Xilinx提供的DDS Compiler知识产权(IP)核与Block Memory Generator知识产权(IP)核实现任意信号及调制信号的输出,以减少设计电路体积。此外,提出引用有限长单位冲激响应(FIR)滤波器插值滤波与相位抖动技术,对输出杂散进行了处理。试验结果表明,信号源频率范围为10 Hz~10 MHz,幅值±5 V可调,无杂散动态范围优于60 dB,频率误差在0.05%以内。该信号源输出信号精度高、杂散低,具有实际的应用价值,也可用于航空发动机、汽轮机等其他动态测试领域。

面向电力线通信的干扰态检测及极化码设计

脉冲干扰噪声是电力线载波通信信道区别于其他通信信道的关键要素,目前的容量研究结果表明,基于马尔可夫模型可以将脉冲干扰噪声信道容量分解为多个干扰态的信道容量与非干扰态信道容量的线性加权叠加,且系统信道容量主要决定于非干扰态信道容量。为此,文中针对典型的脉冲干扰噪声信道推导了信道干扰状态的对数似然比检测准则,在此基础上通过对接收信号干扰态的对数似然比置零提出了脉冲干扰噪声信道的删除信道等效模型。最终,通过等效删除信道模型给出了脉冲干扰噪声信道的极化码设计准则。仿真结果表明,文中提出的干扰状态检测及对数似然比置零的信号预处理非常有效,所设计的极化码在预处理下性能优异,比公开文献中所报道的极化码性能有显著提升。

结合脉冲响应重塑和期望最大化的盲信号分离

多通道欠定卷积语音混合信号的分离问题是盲信号分离领域的难点.由于混合信号中常伴随声学回声和混响,真实的源信号很难完全被清晰地分离出来.传统的盲信号分离算法多数适用于低混响,而在高混响场景下,算法的分离性能极速下降甚至是失效的.本文针对具有声学回声和混响环境下的多通道欠定卷积语音混合信号的分离问题,提出一种结合脉冲响应重塑和期望最大化的盲信号分离算法,该算法在低混响和高混响下都表现出很好的分离性能.首先,利用基于无穷范数和p-范数的脉冲响应重塑技术设计预滤波器消除可听回声,完成对混合信号的重塑,提高混合信号的质量.然后,对重塑后的混合信号利用分层聚类方法估计混合矩阵,基于期望最大化算法框架,设计新的模型参数实时更新规则,通过结合脉冲响应重塑和期望最大化重构源信号.实验结果表明,所提算法可以有效地分离不同混响环境下带声学回声的欠定卷积混合信号,其分离性能优越,同时对噪声具有很好的鲁棒性.

结合小波变换与数学形态学的电缆局放信号识别与降噪方法

针对大部分电缆局放(PD)信号识别方法在噪声影响下的识别准确率低、波形失真等问题,提出了一种基于小波变换与数学形态学的电缆PD信号识别及降噪方法。该方法利用最大重叠离散小波变换提取PD信号的高频和低频特征,结合重构和数学形态法滤除噪声。利用自适应神经网络学习小波变换后的特征,最终完成PD信号的识别分类。基于某变电站实测PD信号波形对所提方法进行实验分析结果表明,信号降噪处理后的信噪比与均方误差分别为5.439 dB、0.251,且整体的识别准确率超过了88%,均优于其他对比方法,具有良好的应用前景。