基于双高斯衰减模型的超声回波处理方法

信号降噪与特征提取是超声检测数据处理的关键技术.基于超声信号有特定结构而噪声和超声信号的结构无关,本文提出一种旨在解决强噪声背景下超声回波的参数估计和降噪问题的方法.该方法将超声回波的参数估计和降噪问题转换为函数优化问题,首先根据工程经验建立超声信号的双高斯衰减数学模型,然后根据观测回波和建立的超声信号模型确定目标函数,接着选择人工蜂群算法对目标函数进行优化从而得到参数的最优估计值,最后由估计出的参数根据建立的超声信号数学模型重构出无噪的超声估计信号.通过仿真和实验表明本文方法可以准确估计出信噪比大于-10 dB的含噪超声回波中的无噪信号,且效果优于基于自适应阈值的小波降噪方法和经验模态分解方法;此外相比常用的指数模型和高斯模型,本文提出的双高斯衰减超声信号模型与实测超声信号更接近,其均方误差为9.4×10~(-5),波形相似系数为0.98.     

超声回波参数估计的初值选取方法

针对超声回波参数估计中,初值随机选取带来的收敛速度慢,搜索效率低,易受噪声影响等问题,对回波参数估计的初值选取进行了探讨。理论上研究了超声回波信号的包络和能量与信号参数的关系,阐明了从信号自身选取参数初值的可行性。在此基础上,利用希尔伯特变换和傅里叶变换提取有效信息,为回波参数估计提供合适的初值,从而减少估计过程的迭代次数、提高参数估计速度。实验结果表明,与传统参数估计方法相比,该方法在不同信噪比情况下都可获得较高的估计精度,而且参数估计的收敛速度较快。      

基于噪声预估计的DPOAE最小二乘估计方法*

针对畸变产物耳声发射(distortion product otoacoustic emission, DPOAE)信号比较微弱,估计结果的准确性对信噪比依赖性强的特点,本文给出一种新的DPOAE信号最小二乘估计(least square estimation, LSE)方法。该方法先估计本底噪声,将本底噪声的影响体现于估计模型中,再估计DPOAE信号。记录信号经过一定的预处理后,利用频域加窗法估计本底噪声并进行噪声衰减;利用IFFT变换后的信号估计DPOAE信号的模型参数,给出DPOAE的强度和相位。仿真和实验结果表明,该方法相对于传统的最小二乘拟合(least-square-fit, LSF)法,对DPOAE的估计更加准确,抗噪声干扰能力更强。     

基于倒谱分析的单水听器目标运动参数估计

基于浅海射线声学多途结构,提出了一种具有高稳健性、高精度的单水听器目标运动参估计方法.针对匀速直线运动目标,综合多途时延差和运动学几何关系,构建了目标三维多途时延模型,进而获得目标运动参数与多途时延差的非线性时间映射.研究了典型水声信道的倒谱表达式,提出了利用倒谱提取多途时延差的策略.采用差分进化综合优化手段估计目标运动参数,提高了算法的稳健性.理论及仿真结果表明,倒谱的时间分辨率不受信号带宽的限制,而主要取决于信号类型和信噪比;CW信号的倒谱对多普勒不敏感;参数估计精度主要取决于时延差估计精度和参与差分进化运算的信息量,当包含有最近点信息时参数估计性能较好.水池实验结果进一步验证了方法的正确性和有效性.     

低信噪比线性调频信号目标的方位估计

线性调频(LFM)信号目标的方位估计是水声探测研究的重要内容,在进行方位估计时,若存在强干扰信号源与强背景噪声,阵元接收信号的信噪比会显著降低,严重影响LFM信号目标方位估计结果的准确性.针对该问题,提出了一种简明分数阶滤波方法,并将其与常规波束形成方法(CBF)相结合来实现低信噪比条件下LFM信号目标的方位估计.简明分数阶傅里叶变换能在正交角度上将LFM信号的能量聚集在特定频点处并形成明显的能量峰,利用该特性,可对阵列各阵元接收的低信噪比LFM信号在简明分数阶域聚集的能量峰进行最佳滤波,以滤除干扰信息及背景噪声.对滤波输出进行逆简明分数阶傅里叶变换可得到增强信干比和信噪比的阵元域信号,进一步用于目标方位估计,就能获得更加准确的目标方位。数值仿真结果和海试实验数据处理结果验证表明,本文所提出的方法可有效抑制干扰和背景噪声,并对低信噪比LFM信号进行准确、稳健的方位估计。      

低信噪比下PSK信号的载频估计（英）

针对电子侦察中PSK信号的载频估计问题,提出一种低信噪比下载频估计新方法。该方法将PSK信号划分为等长度的交叠区间,提取各个区间内信号频谱的聚集性测度作为特征参数,然后将此特征参数导入网格密度聚类算法,以聚类结果作为载频估计特征类,最后将特征类对应的频谱叠加后提取峰值得出信号载频估计值。该方法避免了传统PSK信号载频估计的非线性运算,显著降低了PSK信号载频估计的信噪比门限,且无需先验知识,适合于电子侦察场合。仿真实验结果证实了该方法在低信噪比下PSK信号载频估计的有效性。     

混沌背景中信号参数估计的新方法

怎样提取混沌背景中信号的参数具有重要意义.在重构的相空间中,叠加有其他信号的混沌信号时间序列重构的点集会偏离混沌吸引子所在光滑流形,依据这一性质并综合利用混沌背景中信号本身的特性,提出一种参数估计的新方法：最小相对奇异值(MRSV)法.该方法先建立逆滤波器,由其输出重构相空间,然后改变其参数,使输出信号在嵌入空间中作局部奇异值分解的相对奇异值最小,来实现参数估计.AR模型参数和正弦信号频率估计的仿真结果验证了该方法的有效性. 关键词： 混沌 参数估计 最小相对奇异值(MRSV) 逆滤波器     

低信噪比下PSK信号的载频估计（英文）

针对电子侦察中PSK信号的载频估计问题,提出一种低信噪比下载频估计新方法。该方法将PSK信号划分为等长度的交叠区间,提取各个区间内信号频谱的聚集性测度作为特征参数,然后将此特征参数导入网格密度聚类算法,以聚类结果作为载频估计特征类,最后将特征类对应的频谱叠加后提取峰值得出信号载频估计值。该方法避免了传统PSK信号载频估计的非线性运算,显著降低了PSK信号载频估计的信噪比门限,且无需先验知识,适合于电子侦察场合。仿真实验结果证实了该方法在低信噪比下PSK信号载频估计的有效性。     

基于改进短时傅里叶变换的磁共振随机噪声消减方法

磁共振测深法(magnetic resonance sounding, MRS)具有无需钻探即可直接探测地下水含量的优势,但是极低的信噪比(signal-to-noise ratio, SNR)限制了该方法的大范围应用,目前的研究工作主要集中在消除MRS信号中的尖峰噪声和工频谐波噪声上,而随机噪声由于其无规律性导致难以消除,但是它的影响不容忽视.目前MRS随机噪声的消减常采用叠加法,需要重复采集数据叠加平均来达到消噪目的,探测时间长且消噪效果有限.针对这一问题,本文提出了一种改进的短时傅里叶变换方法,该方法通过处理单次采集的MRS包络信号来降低数据量,提高数据处理效率.改进的短时傅里叶变换方法采用解析信号代替常规短时傅里叶变换中的实值信号,提高MRS信号时频域瞬时幅度的准确度,得到MRS信号的高精度时频分布,然后提取时频域峰值幅度和峰值相位重构信号来消除随机噪声.仿真实验和实测数据处理结果表明,该方法能够直接处理单次采集数据,在信噪比高于–17.21 dB的情况下可有效提取MRS信号,实现随机噪声的压制,且与传统叠加法相比,信噪比最多可提高27.88 dB,均方根误差最多缩小36.44倍,参数估计值更加准确.本文的研究结果为利用MRS获取准确的地下水分布情况奠定了良好的基础.     

一种改善MVDR波束形成性能的方法

最小方差信号无畸变响应(MVDR)波束形成具有最佳的信号保护、干扰消除和噪声降低能力,即信号/(干扰+噪声)比增益最大,它的能量输出作为一种波数谱(方位)估计器,其估计精度或分辨力受着声场信噪比因素的限制.本文提出一种新的改进MVDR波束形成方位分辨力的方法,它可以调节这种波束形成器权向量中的增强因子(等价于改善声场的信噪比条件),从而改进其方位分辨能力,并且不以牺牲信号/(干扰+噪声)比增益为代价。     

快速正交搜索算法在水声信号处理中的应用*

信号的谱估计技术作为一种十分重要且应用广泛的信号分析处理手段,对其性能的不断提升和改进一直是水声信号处理领域研究的重点和热点。对此,该文将快速正交搜索算法应用于水声信号的谱估计中,通过隐式正交对参数进行搜索,实现对信号频率、幅度、相位的估计。仿真和实验数据处理结果表明,相较于传统的谱估计方法,该方法能稳定且准确地估计水声信号的多个参数,获得频率分辨率较高的谱估计结果。     

基于改进非局部均值的CPMG信号去噪算法

采用低场核磁共振技术进行检测时,接收到的回波信号微弱且信噪比低,真实的信号容易淹没在背景噪声中,严重影响到后续的反演等操作的准确性．针对这一问题,提出利用非局部均值滤波算法对CPMG(Carr Purcell Meiboom Gill)回波信号进行降噪的方法．首先,对算法中至关重要的参数选择的方法进行分析,提出了利用Stein无偏风险估计的自适应参数选取方法;然后,根据回波信号的特性对算法进行改进,即利用信号点数据方差的不同,自适应地求取各点进行非局部均值滤波时的相似窗宽度;最后,求取利用最优参数进行降噪后的CPMG回波信号．对仿真数据和真实数据的反演结果对比分析表明,该改进的非局部均值滤波算法能够取得更好的滤波效果,能够获得较优的反演谱．     

Blind parameter estimation of pseudo-random binary code-linear frequency modulation signal based on Duffing oscillator at low SNR

Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBCLFM) signals require prior knowledge, are computationally complex, and exhibit poor performance at low signal-to-noise ratios(SNRs). To overcome these problems, a blind parameter estimation method based on a Duffing oscillator array is proposed. A new relationship formula among the state of the Duffing oscillator, the pseudo-random sequence of the PRBC-LFM signal, and the frequency difference between the PRBC-LFM signal and the periodic driving force signal of the Duffing oscillator is derived, providing the theoretical basis for blind parameter estimation. Methods based on amplitude method, short-time Fourier transform method, and power spectrum entropy method are used to binarize the output of the Duffing oscillator array, and their performance is compared. The pseudo-random sequence is estimated using Duffing oscillator array synchronization, and the carrier frequency parameters are obtained by the relational expressions and characteristics of the difference frequency. Simulation results show that this blind estimation method overcomes limitations in prior knowledge and maintains good parameter estimation performance up to an SNR of-35 d B.     

A novel power spectrum calculation method using phase-compensation and weighted averaging for the estimation of ultrasound attenuation

An estimation of ultrasound attenuation in soft tissues is critical in the quantitative ultrasound analysis since it is not only related to the estimations of other ultrasound parameters, such as speed of sound, integrated scatterers, or scatterer size, but also provides pathological information of the scanned tissue. However, estimation performances of ultrasound attenuation are intimately tied to the accurate extraction of spectral information from the backscattered radiofrequency (RF) signals. In this paper, we propose two novel techniques for calculating a block power spectrum from the backscattered ultrasound signals. These are based on the phase-compensation of each RF segment using the normalized cross-correlation to minimize estimation errors due to phase variations, and the weighted averaging technique to maximize the signal-to-noise ratio (SNR). The simulation results with uniform numerical phantoms demonstrate that the proposed method estimates local attenuation coefficients within 1.57% of the actual values while the conventional methods estimate those within 2.96%. The proposed method is especially effective when we deal with the signal reflected from the deeper depth where the SNR level is lower or when the gated window contains a small number of signal samples. Experimental results, performed at 5 MHz, were obtained with a one-dimensional 128 elements array, using the tissue-mimicking phantoms also show that the proposed method provides better estimation results (within 3.04% of the actual value) with smaller estimation variances compared to the conventional methods (within 5.93%) for all cases considered.     

Modal parameter identification through Gabor expansion of response signals

A new method of identifying modal parameters by decomposing response signals with Gabor transform is presented in this paper to estimate natural frequencies, damping ratios and mode shapes of linear time invariant systems. According to Gabor expansion theory, responses of a multi-degree-of-freedom system can be decomposed into uncoupled signal components, each vibrating at a single natural frequency. From these uncoupled signals, modal parameters are subsequently extracted with common methods. The proposed method can process stationary and non-stationary responses and requires no input signal except for the response signals generated by unknown excitation acting on a system. In the sense of less restriction on the in-out signals, the approach based on time-frequency decomposition is very general. A simulation study on a simply supported beam under non-stationary excitation has demonstrated that the proposed method is effective in parameter estimation.     

Algorithm for Optimal Estimation of Appearance Times of Pulsed Signals in Discrete Time

Using the methods of optimal nonlinear Markov filtering, we obtain an algorithm for optimal mean-square estimation of appearance times of random pulsed variations in signal parameters against the background of white Gaussian noise in discrete time. Linear difference equations are used to describe signals, noise, and the observed processes. Equations of the algorithm permitting real-time calculations of the a posteriori variances and optimal estimations of pulse-appearance times are obtained in the approximation of Gaussian conditional probability densities. We present simulation results for algorithm operation in the particular problem of estimating the appearance times of two pulsed signals having the known shapes and observed against noise background.     

混沌噪声背景下微弱脉冲信号的检测及恢复

构建了一种在混沌噪声背景下检测并恢复微弱脉冲信号的模型.首先,基于混沌信号的短期可预测性及其对微小扰动的敏感性,对观测信号进行相空间重构、建立局域线性自回归模型进行单步预测,得到预测误差,并利用假设检验方法从预测误差中检测观测信号中是否含有微弱脉冲信号.然后,对微弱脉冲信号建立单点跳跃模型,并融合局域线性自回归模型,构成双局域线性(DLL)模型,以极小化DLL模型的均方预测误差为目标进行优化,采用向后拟合算法估计模型的参数,并最终恢复出混沌噪声背景下的微弱脉冲信号.仿真实验结果表明本文所建的模型能够有效地检测并恢复出混沌噪声背景中的微弱脉冲信号.     

Cross Psi(B)-energy operator-based signal detection

In this paper, two methods for signal detection and time-delay estimation based on the cross Psi(B)-energy operator are proposed. These methods are well suited for mono-component AM-FM signals. The Psi(B) energy operator measures how much one signal is present in another one. The peak of the Psi(B) operator corresponds to the maximum of interaction between the two signals. Compared to the cross-correlation function, the Psi(B) operator includes temporal information and relative changes of the signal which are reflected in its first and second derivatives. The discrete version of the continuous-time form of the Psi(B) operator, which is used in its implementation, is presented. The methods are illustrated on synthetic and real signals and the results compared to those of the matched filter and the cross correlation. The real signals correspond to impulse responses of buried objects obtained by active sonar in iso-speed single path environments.     

特征提取方法在多源混合信号分选中的应用

在复杂电磁环境中,脉冲的大量丢失(数据缺失)及信号参数空间的严重交叠,破坏了传统分选方法所利用的信号规律性,最终导致现有信号分选算法很难获得令人满意的分选效果甚至完全失效。在同时考虑多个关键特征指标(脉冲宽度、载波频率、到达时间)的基础上,设计多参数五层互耦的分选算法;特别是提出新的关键特征指标提取方法,研究数据内部蕴含的特征向量,建立相应的数学模型,最终应用于对各种通信信号的分选。通过数值结果可以看出,引入的五步分选法可以实现对严重交叠和部分数据缺失的信号的分选。     

基于粒子滤波的单通道正弦调频混合信号分离与参数估计

以单通道正弦调频(SFM)混合信号为研究对象,提出了基于粒子滤波的正弦调频混合信号分离与参数提取方法.针对正弦调频混合信号频率无跳变的特征,提出了一种基于粒子滤波的相位差解混叠算法,并通过源信号相位差解决了本算法中粒子滤波高维状态空间降维问题,提出了一种适合高维状态空间的似然函数模型,比较固定长度粒子估计值和真实值误差,进而准确衡量粒子权重.通过在重采样后引入MCMC转移,解决了静止参数下粒子多样性降低问题,有效提高粒子滤波迭代收敛速度.从而在先验知识仅已知信号调制方式的情况下,完成对单通道正弦调频混合信号的参数提取,并通过重构信号完成正弦调频混合信号分离.最后通过仿真分析发现,该方法能够有效的实现正弦调频混合信号的分离与参数估计.