石油管道超声检测信号的两级自适应噪声消除算法

针对石油管道缺陷超声检测信号的噪声消除问题, 研究了一种两级自适应噪声消除算法. 第一级自适应滤波器作为预处理级, 使信号获得较好的相关性和信噪比, 确保第二级自适应滤波器获得更优的性能. 实测超声信号两级自适应滤波结果表明: 两级自适应滤波算法能有效增强超声检测信号中的缺陷信号成分, 显著提高信噪比.     

Statistical analysis of split spectrum processing for multiple target detection

This work provides a statistical analysis of the performance of split spectrum processing (SSP) for the detection of multiple targets using data consisting of simulated flaw signals added to experimentally obtained backscattered grain noise. The investigation is performed under two conditions: known a priori target spectral characteristics (i.e., center frequency and bandwidth) which, in turn, identifies the optimal spectral range for processing, and adaptively obtaining the processing frequencies using group delay moving entropy. The group delay moving entropy method was introduced to select the optimal frequency regions for SSP when detecting multiple targets. The effectiveness of this technique is statistically demonstrated in this paper. The performance is measured in terms of normalized signal-to-noise ratio (SNR) and probability of target detection. SSP with known target information yields a slightly higher probability of detection compared to SSP using group delay moving entropy, while both cases achieve comparable SNR enhancement. The SSP results were also compared with the corresponding bandpass filter outputs, which show superior performance for SSP for a wide range of simulation parameters.     

一种低信噪比环境下的语音端点检测算法

端点检测技术是语音信号处理的关键技术之一，为提高低信噪比环境下端点检测的准确率和稳健性，提出了一种非平稳噪声抑制和调制域谱减结合功率归一化倒谱距离的端点检测算法。该算法首先通过抑制非平稳噪声再采用调制域谱减消除残余噪声来提升信噪比，减少语音失真。然后再提取每帧信号的功率归一化倒谱系数，计算每帧信号与背景噪声的功率归一化倒谱距离。最后将该倒谱距离作为检测参数，采用双门限判决方法进行端点检测。实验结果表明，该端点检测算法对语音帧和噪声帧具有较好的区分性。此外，在低信噪比环境下，所提出的算法对于不同类型的噪声都具有较好的稳健性。     

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.     

Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection

The utilization of signal processing techniques in nondestructive testing, especially in ultrasonics, is widespread. Signal averaging, matched filtering, frequency spectrum analysis, neural nets, and autoregressive analysis have all been used to analyze ultrasonic signals. The Wavelet Transform (WT) is the most recent technique for processing signals with time-varying spectra. Interest in wavelets and their potential applications has resulted in an explosion of papers; some have called the wavelets the most significant mathematical event of the past decade. In this work, the Wavelet Transform is utilized to improve ultrasonic flaw detection in noisy signals as an alternative to the Split-Spectrum Processing (SSP) technique. In SSP, the frequency spectrum of the signal is split using overlapping Gaussian passband filters with different central frequencies and fixed absolute bandwidth. A similar approach is utilized in the WT, but in this case the relative bandwidth is constant, resulting in a filter bank with a self-adjusting window structure that can display the temporal variation of the signal's spectral components with varying resolutions. This property of the WT is extremely useful for detecting flaw echoes embedded in background noise. The detection of ultrasonic pulses using the wavelet transform is described and numerical results show good detection even for signal-to-noise ratios (SNR) of -15 dB. The improvement in detection was experimentally verified using steel samples with simulated flaws.     

Characterization of Structural Noise Patterns and Echo Separation in the Time-Frequency Plane for Austenitic Stainless Steels

The aim of this study is to characterize the structural noise for a better flaw detection in heterogeneous materials (steels, weld, composites...) using ultrasonic waves. For this purpose, the continuous wavelet transform is applied to ultrasonic A-scan signals acquired using an ultrasonic non destructive testing (NDT) device. The time-scale representation provided, which highlights the temporal evolution of the spectral content of the A-scan signals, is relevant but can lead to misinterpretation. The problem is to identify if each pattern from the wavelet representation is due to the structural noise or the flaw. To solve this problem, a detection technique based on statistical significance testing in the time-scale plane is used. Information about the structural noise signals is injected into the decision process using an autoregressive model, which seems relevant according to the spectral content of the signal. The approach is tested on experimental signals, obtained by ultrasonic NDT of metallic materials (austenitic stainless steel) then on a weld in this steel and indeed enables to distinguish the components of the signal as flaw echoes, which differ from the structural noise.     

模拟频域滤波重构直方图均衡的图像增强方法

针对传统图像增强方法的不足,提出一种基于模拟频域滤波重构直方图均衡的图像增强方法.将频率域滤波的思想引入空间域直方图运算当中,在模拟频率直方图统计中进行频率信息的统计,利用这些频率信息建立模拟频率域坐标,进行模拟频域滤波,对滤波处理后的直方图进行均衡化处理.实验表明:与传统方法相比,该算法优化了灰度级的动态分布范围,能得到更清晰的增强效果,且在图像中没有视觉明显的噪声放大.     

低信噪比环境下的语音识别方法研究

单通道语音信号在信噪比较大的环境下经过增强后再识别,能表现出较高的识别率。但是在低信噪比环境下,增强后语音信号的识别率急剧下降。针对此种情况,提出了一种用在识别系统前端的语音增强算法,该增强算法将采集到的带噪语音信号先使用对数最小均方误差(Logarithmic Minimum Mean Square Error,Log MMSE)提高其信噪比,然后再利用改进的维纳滤波去除噪声残留并提升语音可懂度,最后用梅尔频率倒谱系数(Mel-Frequency Cepstral Coefficients,MFCC)和隐马尔科夫模型(Hidden Markov Model,HMM)对增强后的语音信号做特征提取并识别。实验分析结果表明,该方法能有效地抑制背景噪声并减少噪声残留,显著提升低信噪比环境下语音识别的准确性。     

Envelope signal selective emphasis in a polar radio frequency transmitter architecture

The linearity of an efficient polar transmitter architecture, with a 1 bit oversampled delta?sigma (DS) modulating the envelope signal, depends, to a high degree, on low-pass envelope filtering. This filter is compulsory to attenuate the DS quantisation noise. A high cut-off frequency results in more noise being included. In contrast, using a filter with a low cut-off frequency results in attenuation of the information content of the envelope signal. Either way, the result is unwanted spectral regrowth. By pre-emphasising the envelope signal, the filter?s attenuation of the information is mitigated. The pre-emphasis is implemented by a digital pseudo-derivative high-pass filter, with inverse magnitude characteristics of the analogue low-pass filter, within a limited interest band. Consequently, the low-pass filter can be designed with a lower cut-off frequency to attenuate more of the DS modulator noise, and the modulator can switch at lower frequencies. With this technique, the WLAN output spectrum, at the critical 30 MHz offset corner frequency, is improved by 12.5 dB, considering a second order DS sampling at 1.28 GHz. The technique was verified with an experimental setup and the behaviour agrees well with simulations.     

水声脉冲信号检测的新方法

水声脉冲信号是由一个运动平台上的发射器发射的。信号的调制方式、载频、幅度、脉宽以及周期均为未知。该文介绍了一种水声脉冲信号检测的新方法。水声脉冲信号接收机输出的短时谱重心是不断起伏的。起伏在高信噪比时会变得很小,而在低信噪比(或无信号)时会变得很大。由于,起伏的绝对偏差移动平均可用来度量起伏的大小,因此,它可以被用来检测水声脉冲信号。还介绍了新检测方法的原理、算法以及仿真结果。在海洋噪声、运动载体的辐射噪声以及小的多途干扰背景中,该方法能可靠地检测水声脉冲信号,并能同时对信号的一些参数作出估计。     

A global optimization technique for microwave nondestructive evaluation

A global optimization technique based on a genetic algorithm is proposed for microwave nondestructive evaluation. Starting from an integral formulation of the inverse scattering problem, the detection of a flaw in a known host medium is reduced to the minimization of a suitable nonlinear functional relating the measured field to the field predicted at a given iteration. The geometrical parameters of the flaw are retrieved by using a tomographic imaging approach. Numerical results are reported concerning cracks in lossless and lossy structures. The effects of the noise on measured input data are also analyzed.     

低信噪比环境下语音识别的鲁棒性方法研究

针对噪声环境下语音识别率急剧下降的问题，提出了一种基于语音时频域稀疏性原理的改进最小方差无畸变响应波束形成与改进维纳滤波结合的算法。该算法首先利用麦克风阵列语音信号的空间信息，通过基于时频掩蔽的改进最小方差无畸变响应波束形成器，增强目标声源方向的语音信号，抑制其他方向噪声的干扰，然后再使用改进的维纳滤波器去除残留噪声并提高语音可懂度，对增强后的语音信号提取梅尔频率倒谱系数作为特征参数，使用隐马尔可夫模型搭建语音识别系统。实验结果表明，该方法能够有效提高低信噪比环境下的语音识别率，具有较好的鲁棒性。     

A successive parameter estimation algorithm for chirplet signal decomposition

In ultrasonic imaging systems, the patterns of detected echoes correspond to the shape, size, and orientation of the reflectors and the physical properties of the propagation path. However, these echoes often are overlapped due to closely spaced reflectors and/or microstructure scattering. The decomposition of these echoes is a major and challenging problem. Therefore, signal modeling and parameter estimation of the nonstationary ultrasonic echoes is critical for image analysis, target detection, and object recognition. In this paper, a successive parameter estimation algorithm based on the chirplet transform is presented. The chirplet transform is used not only as a means for time-frequency representation, but also to estimate the echo parameters, including the amplitude, time-of-arrival, center frequency, bandwidth, phase, and chirp rate. Furthermore, noise performance analysis using the Cramer Rao lower bounds demonstrates that the parameter estimator based on the chirplet transform is a minimum variance and unbiased estimator for signal-to-noise ratio (SNR) as low as 2.5 dB. To demonstrate the superior time-frequency and parameter estimation performance of the chirplet decomposition, ultrasonic flaw echoes embedded in grain scattering, and multiple interfering chirplets emitted by a large, brown bat have been analyzed. It has been shown that the chirplet signal decomposition algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements. Numerical and analytical results show that the algorithm is efficient and successful in high-fidelity signal representation.     

信号相位匹配算法的语音降噪及性能评估

针对信号相位匹配算法能够在信号估计中准确提取信号频率成分的优点,将三子阵信号相位匹配算法应用于提取强背景噪声中的语音信号,同时提出了基于平均段内功率谱密度距离的语音质量客观评价方法,将主观平均意见分（MOS）、信噪比和平均分段功率谱密度距离作为指标,分别对三子阵信号相位匹配法、谱减法和最小均方误差估计法这三种算法的处理结果进行客观评价并对算法的降噪性能做了对比。仿真结果表明,三子阵相位匹配算法能够达到强背景噪声环境下语音降噪的目的;同时基于平均段内功率谱密度距离的语音质量客观评价结果与主观评价结果相符合,该评价方法具有一定的可行性。     

基于水平分置线性双阵列的超声全聚焦成像方法在粗晶材料检测中的应用

针对粗晶材料超声检测信噪比低的问题,提出了一种水平分置线性双阵列超声成像方法。将两个线阵超声换能器沿直线水平分置在待检区域表面两侧,用收发分离的信号采集模式,一侧激发,另一侧记录各通道数据,进行聚焦成像。相比单阵列和同位置双线阵检测,文中的方法有效地减少了背向散射信号对缺陷信号的干扰,提高了成像信噪比。在粗晶铜质试块上的成像实验结果表明,当缺陷距离阵列较近时,文中的方法优于单阵列和同位置双线阵方法,成像信噪比提高约5～10 dB;当缺陷距离阵列较远时,单阵列模式和同位置双线阵检测方法失效,但文中的方法依然可以识别缺陷。文中的研究为粗晶材料的超声检测提供了一种可行的方案。     

基于双树复小波包峭度图的轴承故障诊断研究

针对传统包络谱和峭度图分析技术的缺陷,提出了一种基于双树复小波包峭度图的轴承故障诊断方法。该方法综合利用了双树复小波包变换和峭度图分析技术,克服了原峭度图方法只采用FIR和短时傅立叶变换滤波器的缺点,提高了从强噪声环境中提取瞬态冲击特征的能力。首先利用双树复小波包变换,将振动信号分解成不同频带的分量,然后计算各小波分量的谱峭度,再利用谱峭度的滤波器作用,计算最大峭度值对应分量信号的包络谱,根据包络谱就可识别齿轮箱轴承的故障部位和类型。齿轮箱轴承故障振动实验信号的研究结果表明：该方法不仅提高了信噪比和频带选择的正确性,而且能有效地识别轴承的故障。     

一种新型的滤波器组在水声应答器中的应用

针对某型水声应答器中处理信号频带变宽,信号中叠加了多个频率分量以及实时性要求高的问题,提出一种基于频域信道化技术的滤波器组方法。首先给出该滤波器组的理论推导,并对滤波器组进行加窗处理,给出了FFT的长度和移动重叠点数等参数对该滤波器组的影响,最后进行了数值仿真,并进行了湖试试验数据处理。结果证明该方法可有效解决信号的信道串漏问题,验证了该滤波器组的正确性和可行性。该方法通过对信号信道化处理,减小了滤波处理的运算量,提高了数据的信噪比,便于后续的目标检测。该滤波器组算法简单、易实现、运算量小,在水声应答器信号处理中有一定的工程借鉴价值。     

A filter for on-line estimation of spectral content

A robust filter algorithm to extract, a posteriori, the rational signal model from a noisy measurement, with little a priori information, is proposed. The spectrum and the statistics of the signal and of the corrupting noise are assumed unknown, except that the signal is assumed to have a rational spectrum. An algorithm based on system and signal theory is derived to select a set of frequencies where the signal-to-noise ratio (SNR) is high from a given measurement spectrum. The density of selected frequencies weights the importance of the measurement as a function of frequency, An estimate of the signal model is obtained from the best weighted least-squares fit to the measurement spectrum at the selected frequencies. The proposed filter has applications to control and signal processing, and a wide variety of applications are presented. Applications include: system identification of a dc motor and a two-link manipulator, extraction of a myo-electric signal from a noisy measurement, the assignment of a rational model to a vegetation tissue's impedance, and to the number density profile of atmospheric oxygen     

一种不需要次级通道建模的有源噪声控制算法

对有源噪声控制的研究,绝大部分都是建立在次级通道已知的基础上,或者通过在线或离线辨识来建模次级通道,通过分析滤波-XLMS（FXLMS）算法收敛的几何特性实现有源噪声的无模型控制。子带技术是将信号通过不同频带的滤波器,把信号分解到不同频带的子带中,对各子带信号分别进行相应的处理,并减少处理时间。将两者相结合,运用子带技术,采用过采样无延迟子带结构消除单频噪声、窄带噪声以及宽带噪声,降噪效果明显。相比传统的XLMS滤波算法,该算法降低了运算量,且实现结构简单。     

基于EMD算法的光栅莫尔条纹信号去噪方法研究

针对计量光栅莫尔条纹信号的质量问题,提出了基于经验模态分解（EMD）算法对非平稳光栅莫尔条纹信号模型的去噪方法。建立非平稳的光栅时变信号模型,利用EMD算法不需要定义滤波器参数的自适应性优点,对添加不同噪声的多组光栅信号模型进行了滤波分析的仿真实验,其信噪比和均方根误差两项指标优于均值滤波、小波阈值去噪方法。对两路正余弦理想信号添加高次谐波分量,通过对比EMD算法抑制高次谐波前后的李萨如图形,验证了该方法在去噪过程中对光栅莫尔条纹信号正弦性误差补偿的良好效果。