一种自适应噪声抵消系统的仿真与设计

自适应噪声抵消系统是基于自适应滤波原理的一种扩展,它能从被噪声干扰的环境中检测和提取有用信号,抑制或衰减噪声干扰,提高信号传递和接收的信噪比质量。研究常用噪声消除算法,并仿真验证系统设计和所选自适应算法的可行性,最后应用DSP技术设计一个自适应噪声抵消系统,使其能消除含噪语音信号中的背景噪声,达到提高语音信号质量的目的。     

水下小孔径阵列远程目标检测方法研究

匹配滤波器频域自适应线谱增强方法是一种基于递归算法的非线性滤波技术,它大大提高了匹配滤波器的检测性能.针对当前该技术使用窄带信号作为发射信号存在可利用的带宽有限,不能充分发挥自适应线谱增强器性能的问题,文章提出将该技术与宽带信号相结合来检测远程目标.仿真显示,该方法在低信噪比条件下获得了较高的信噪比增益和检测概率.海试...     

一种强噪声背景下的微弱信号检测的新方法

基于自适应滤波算法对小波滤波器的构造方法进行了研究，重点在于解决对强噪声环境下微弱信号提取所需要的小波变换的子带自适应滤波器的构造，把小波变换技术和自适应滤波技术应用于微弱信号的检测。提出基于自适应小波变换的心电信号的检测，利用小波变换的子带编码理论，通过在多个子带权值的自适应匹配，回复后拟合微弱信号。仿真结果表明，该方法可进一步改善信号的检测能力，在检测微弱信号的特征和改善信噪比方面是一种十分有效的方法。     

金属表面裂纹脉冲压缩增强型激光超声检测方法研究

为了提高激光超声技术检测能力,提出了一种基于脉冲压缩的增强型激光超声检测方法,并将其用于金属表面裂纹检测。该方法结合激光束空间分布调控技术,激发线性调频超声表面波信号,进一步利用匹配滤波算法对线性调频激光超声表面波信号进行脉冲压缩,可大幅度提高激光超声检测信号的信噪比和分辨率。建立空间调制激光超声数值计算模型,研究线性调频激光超声信号激励方法;基于数值模拟获得的线性调频激光超声检测信号,开发基于匹配滤波器的激光超声脉冲压缩信号处理方法;通过数值模拟不同表面裂纹缺陷与不同空间调制情况下的脉冲压缩信号效果,探究该方法对于激光超声检测信号信噪比的增强能力,并最终通过试验验证。结论表明,基于脉冲压缩技术的增强型激光超声检测方法,可以大幅度抑制噪声信号,提高针对金属表面裂纹的缺陷检测能力,解决激光超声无损检测信噪比低,灵敏度差的问题,也为该方法进一步研究提供理论依据和试验指导。     

基于RLS的自适应阵列抗交叉串扰语音增强研究

在噪声环境下的麦克风阵列语音增强应用中,由于语音与噪声之间存在交叉串扰,传统自适应算法降噪性能受到很大的影响。为了提高自适应滤波器收敛性能,同时抑制麦克风之间语音信号的交叉串扰,采用两级抗交叉串扰麦克风阵列结构,自适应滤波器均采用RLS格型一阶梯算法。其中,第～级滤波系统仅在确认为无语音阶段进行参数调整,以充分消除噪声的影响。实验结果表明,该方法消噪量大,对语音损伤小,语音增强效果显著。     

自适应串扰噪声消除技术在小孔径钻孔超声波检测中的应用

介绍了一种基于自适应滤波的噪声消除技术,分析了超声波用于小孔径钻孔检测中存在的同频串扰噪声的生成原因以及它对测量的影响,并对串扰噪声信号和待测信号进行建模;介绍了自适应噪声对消的原理,并针对应用给出了定步长自适应噪声的消除算法以及改进后的变步长自适应噪声消除算法,对两种算法在自适应学习速率上进行了比较,并给出经自适应去噪后的信号和原始信号的对比结果。     

基于提升小波变换和AdaBoost的超声信号消噪技术

为了提高超声无损检测(UNDT)和无损评价(UNDE)中基础数据的信噪比(SNR),提出了一种基于提升小波变换和AdaBoost模式识别理论的超声信号消噪技术.该技术在研究材料内部散射体引起的结构噪声产生机理,以及分析传统裂谱分析算法局限性的基础上,利用提升小波变换将原始超声检测信号分解到小波空间后,通过采用AdaBoost算法构成的信噪分离器对信号和噪声进行识别、分离来消除噪声,得到高信噪比的超声回波信号.实验结果表明,与传统裂谱分析算法相比,该技术提高了消噪性能的稳定性,增强了湮没材料内部各种散射体散射中的缺陷回波信号能力.     

去除脉冲噪声的自适应开关中值滤波

为消除图像中的脉冲噪声,提出了自适应开关中值(ASM)滤波算法。该算法采用一种新的噪声检测方法将图像中的像素分为信号点和噪声点两类。对检测出的噪声点统计其个数并由此估算图像中的噪声密度,根据估计的噪声密度自适应确定滤波窗口尺寸,采用改进的中值滤波对检测出的噪声点进行处理;而信号点则保留其灰度值不予处理。对ASM滤波进行仿真实验,结果表明,它能在有效去除噪声的同时很好地保护图像细节,较传统中值滤波及其它改进中值滤波算法有更优的滤波性能。     

基于包络滤波的电磁超声检测数据降噪算法

针对电磁超声检测中的噪声问题,提出了基于包络滤波技术的噪声消除方法。该方法利用三次多项式插值曲线拟合法求取原始信号的包络曲线,按照设定的降噪阈值用上下包络线分别分段地对原始信号进行修正,最后对上下包络修正的结果取平均,从而实现平滑信号和抑制杂波的作用。该方法的处理过程不会损伤信号的主体特征,对于混叠在信号中的多种类型的高频噪声都能进行很好的抑制。利用电磁超声表面波的实验数据对算法进行了检验,实验结果表明,基于包络滤波的降噪算法可以有效地消除检测信号中的杂波和毛刺干扰,抑制噪声的幅度,提高信噪比。     

基于最大相关熵准则的水下生物脉冲噪声消除方法

近海生物噪声表现为脉冲噪声,这一类噪声显著降低了水声通信系统的性能。结合水声信道的稀疏特性,提出一种基于最大相关熵准则的级联滤波器算法,该算法实现了脉冲噪声的消除及稀疏信道估计。第一级自适应滤波器完成对噪声的消除工作,第二级滤波器对去噪后的信号进行稀疏信道估计。该方法的优势在于无需知道信号和噪声的相关信息。仿真结果表明,提出的方法比单一的滤波器结构和传统信道估计算法性能更加优越。     

Spectral histogram using the minimization algorithm-theory and applications to flaw detection

In ultrasonic flaw detection in large grained materials, backscattered grain noise often masks the flaw signal. To enhance the flaw visibility, a frequency diverse statistical filtering technique known as split-spectrum processing has been developed. This technique splits the received wideband signal into an ensemble of narrowband signals exhibiting different signal-to-noise ratios (SNR). Using a minimization algorithm, SNR enhancement can be obtained at the output. The nonlinear properties of the frequency diverse statistic filter are characterized based on the spectral histogram, which is the statistical distribution of the spectral windows selected by the minimization algorithm. The theoretical analysis indicates that the spectral histogram is similar in nature to the Wiener filter transfer function. Therefore, the optimal filter frequency region can be determined adaptively based on the spectral histogram without prior knowledge of the signal and noise spectra.     

基于信息融合与FastICA的轴承故障提取方法

针对振动传感器监测信号易受噪声干扰的问题,提出一种基于FastICA算法与信息融合的轴承故障诊断方法。算法对各通道测得的信号采用FastICA算法进行降噪处理,采用自适应线性加权算法对降噪后信号进行数据层信息融合,最后基于谱峭度指标设计自适应带通滤波器,进行特征提取。此方法解决了低信噪比条件下的轴承故障特征提取问题。使用了仿真和实验轴承故障信号验证了算法的有效性。     

钢包耳轴根部焊缝的相控阵超声检测与信号处理

采用匹配追踪算法和小波变换对低信噪比(2dB和-5dB)钢包耳轴根部焊缝缺陷检测信号进行预处理对比分析,在定制试块上采用64阵元超声相控阵探头对深度为230mm的耳轴根部焊缝进行检测,并且结合基于Gabor原子库的匹配追踪算法对回波信号进行消噪后成像.结果表明,在低信噪比(-5dB)条件下,匹配追踪算法较小波变换有更好的预处理效果;对深度位于220mm和230mm处的钢包耳轴焊缝缺陷的实际检测信号进行去噪处理时,匹配追踪算法能够准确定位缺陷位置并显著提高缺陷处信噪比.     

Adaptive ultrasonic measurement of blood vessel diameter and wall thickness: theory and experimental results

An adaptive ultrasonic technique for measuring blood vessel diameter and wall thickness is presented. This technique allows one to use a target-specific transmitted waveform/receiver filter to obtain a larger signal-to-noise ratio (SNR) in the received signal than conventional techniques. Generally, SNR of a received wave increases as the intensity of the transmit wave increases; however, because of the FDA limitations placed on the amount of transmit energy, it is important to be able to make the most efficient use of the energy that is available to obtain the best possible SNR in the received signal. Adaptive ultrasonic measurement makes the most efficient use of the energy that is available by placing the maximum amount of energy in the largest target scattering mode. This results in more energy backscatter from a given target, which leads to a higher SNR in the received waveform. Computer simulations of adaptive ultrasonic measurement of blood vessel diameter show that for a SNR of 0 dB in the transmitted waveform, the standard deviation of the diameter measurements for a custom-designed transmitted waveform is about two orders of magnitude less than the standard deviation of the diameter measurements using more conventional waveforms. Diameter and wall thickness measurement experiments were performed on a latex tube and a bovine blood vessel using both custom-made and conventionally used transmitted waveforms. Results show that the adaptively designed waveform gives a smaller uncertainty in the measurements. The adaptive ultrasonic blood vessel diameter and wall thickness measuring technique has potential applications in examining vessels which are either too deep inside the body or too small for conventional techniques to be used, because of the low SNR in the received signal.     

基于高阶累积量自适应算法的列车轴承的故障诊断

论述三阶累积量递推最小二乘自适应算法（CDRLS）和三阶累积量最小均方误差自适应算法（CDEFWLMS），及其在列车轴承故障诊断中的应用。通过对列车滚动轴承的保持架断裂和滚子掉块典型故障信号分析，得出高阶累积量自适应算法具有良好的降噪性，CDEFWLMS算法比CDRLS算法处理后的特征频率更突出明显。研究表明：用高阶累积量自适应滤波算法提取信号特征，容易地分离正常轴承信号、保持架断裂和滚子掉块故障信号，从而验证了高阶累积量自适应滤波在故障诊断、检测中具有良好的应用特性。     

Ultrasonic data compression via parameter estimation

Ultrasonic imaging in medical and industrial applications often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data and to facilitate the analysis and remote access of ultrasonic information. The precise data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. In this study, a successive parameter estimation algorithm based on a modified version of the continuous wavelet transform (CWT) to compress and denoise ultrasonic signals is presented. It has been shown analytically that the CWT (i.e., time x frequency representation) yields an exact solution for the time-of-arrival and a biased solution for the center frequency. Consequently, a modified CWT (MCWT) based on the Gabor-Helstrom transform is introduced as a means to exactly estimate both time-of-arrival and center frequency of ultrasonic echoes. Furthermore, the MCWT also has been used to generate a phase x bandwidth representation of the ultrasonic echo. This representation allows the exact estimation of the phase and the bandwidth. The performance of this algorithm for data compression and signal analysis is studied using simulated and experimental ultrasonic signals. The successive parameter estimation algorithm achieves a data compression ratio of (1-5N/J), where J is the number of samples and N is the number of echoes in the signal. For a signal with 10 echoes and 2048 samples, a compression ratio of 96% is achieved with a signal-to-noise ratio (SNR) improvement above 20 dB. Furthermore, this algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements ranging from 10 to 60 dB for composite signals having SNR as low as -10 dB.     

EMAT noise suppression using information fusion in stationary wavelet packets

An important issue in ultrasonic nondestructive testing is the detection of flaw echoes in the presence of background noise created by instrumentation and by clutter noise. Signal averaging, autoregressive analysis, spectrum analysis, matched filtering, and the wavelet transform have all been used to filter noise in ultrasonic signals. Widely-used wavelet threshold estimation algorithms are not designed for electromagnetic acoustic transducer (EMAT) pulse-echo signals, and therefore do not exploit their unique impulse nature. The approach to ultrasonic signal filtering proposed in this paper is based on stationary wavelet packet denoising with a threshold influenced by several information sources: a statistical echo detection, the amplitude distribution of the wavelet transform coefficients, and a priori known system frequency characteristics. The proposed method was evaluated on signals measured with EMAT probes and under various SNR conditions; it outperforms the wavelet transform with the Stein unbiased risk estimate (SURE) threshold estimation method and split-spectrum processing (SSP). The results indicate SNR enhancement of 19 dB with real EMAT data.     

基于小波变换的裂谱分析法

 为了提高超声无损检测（UNDT）和无损评价（UNDE）中基础数据的信噪比（SNR）,提出了一种基于小波变换多分辨率分析的裂谱分析新方法．该方法在分析传统裂谱分析（SSP）方法原理及其局限性的基础上,通过采用小波变换多分辨率分析能力将原始超声回波信号进行等Q子带分解,然后按照一定的信噪分离规则来消除噪声,达到提高信噪比的目的．实验结果表明,与传统裂谱分析方法相比,该方法提高了消噪性能的稳定性,增强了湮没晶粒(或其他散射体)散射中的缺陷回波信号能力．     

A measurement method based on Kalman filtering for ultrasonic time-of-flight estimation

A new digital signal-processing method for ultrasonic time-of-flight (TOF) estimation is presented. The method applies the discrete extended Kalman filter (DEKF) to the acquired ultrasonic signal in order to accurately estimate the shape factors of the echo envelope as well as locate its onset. It is also possible to assure reduced bias and uncertainty in critical TOF measurements, such as those involving low signal-to-noise ratio (SNR) as well as severe distortion of echo shape. A number of numerical tests are conducted on simulated signals with the aim of highlighting the good performance of the method when operating in critical conditions. Results attained in TOF-based distance measurements finally assess the reliability and efficacy of the method in the presence of actual ultrasonic signals.