Fast curve estimation using preconditioned generalized Radontransform

A new algorithm for fast curve parameter estimation based on the generalized Radon transform is proposed. The algorithm works on binary images, obtained, e.g., by edge filtering or deconvolution. The fundamental idea of the suggested algorithm is the use of a precondition map to reduce the computational cost of the generalized Radon transform. The precondition map is composed of irregular regions in the parameter domain, which contain peaks that represent curves in the image. To generate the precondition map, a fast mapping procedure named image point mapping is developed. As the image point mapping scheme maps image points into the corresponding parameter values in the parameter domain, it is possible to improve computational efficiency by recognizing image points with value zero. Initially, the suggested algorithm estimates the precondition map and subsequently applies the generalized Radon transform within the regions specified by the precondition map. The required parameter domain sampling and the resulting blurring are also investigated. The suggested algorithm is successfully applied to the identification of hyperbolas in seismic images, and two numerical examples are given.     

Convergence of the Red-TOWER method for removing noise from data

By coupling the wavelet transform with a particular nonlinear shrinking function, the Red-telescopic optimal wavelet estimation of the risk (TOWER) method is introduced for removing noise from signals. It is shown that the method yields convergence of the L₂ risk to the actual solution with optimal rate. Moreover the method is proved to be asymptotically efficient when the regularization parameter is selected by the generalized cross validation criterion (GCV) or the Mallows criterion. Numerical experiments based on synthetic data are provided to compare the performance of the Red-TOWER method with hard-thresholding, soft-thresholding, and neigh-coeff thresholding. Furthermore, the numerical tests are also performed when the TOWER method is applied to hard-thresholding, soft-thresholding, and neigh-coeff thresholding, for which the full convergence results are still open     

Alpha稳定分布下基于RHMy滤波的MQAM信号码元速率估计方法

针对Alpha稳定分布噪声环境下,传统的MQAM信号码元速率估计方法性能恶化甚至完全失效的问题,本文结合了改进的Myriad类滤波与小波变换法的优点,提出一种基于递归混合Myriad滤波(Recursive Hybrid Myriad filter, RHMy)的高阶消失矩二次小波变换法对MQAM信号进行码元速率估计。首先采用RHMy滤波充分抑制噪声中的脉冲成分,再采用高阶消失矩小波估计码元速率。仿真结果表明,在Alpha稳定分布噪声环境中,相比于多尺度Haar小波变换法、分数低阶循环谱法与基于加权Myriad滤波(Weighted Myriad filter, WMy)的小波变换法,该方法有效的提高了低广义信噪比下的估计精度,同时在不同的脉冲噪声条件下具有较好的鲁棒性。      

基于小波变换的MPSK信号符号率估计方法

通过对MPSK信号的小波域特征进行分析,研究了小波变换结合FFT估计MPSK信号的符号率的算法,比较了利用一次小波和二次小波变换估计信号符号率的性能。仿真结果表明,在相同情况下,利用MPSK信号的二次小波变换进行符号率估计比利用一次小波变换进行符号率估计的性能更为优越。在信噪比不小于5dB的情况下,利用信号的二次小波变换提取信号符号率的准确率能够达到98％以上。     

基于小波变换的MSK信号码速率盲估计

针对最小频移键控调制信号的码速率估计问题,提出一种基于Haar小波变换的MSK信号码速率盲估计方法。首先对接收信号作傅里叶变换得到信号频谱,对频谱频点分析粗估计信号的码速率,接着通过粗估计的码速率选取短时傅里叶变换窗函数长度和3个小波尺度,利用短时傅里叶变换得到信号瞬时频率变化,再利用小波的边缘检测特性对信号瞬时频率序列相位跳变点检测,最后对检测结果作频谱分析,估计频率得到MSK信号的码速率。仿真结果表明,高于信噪比门限时本算法可以对MSK信号码速率有效估计。     

Real-Time Simultaneous Estimation and Decomposition of Random Signals

In this paper, an efficient algorithm is derived for multiresolutional estimation and decomposition of noisy random signals. This algorithm performs in real-time the estimation and decomposition simultaneously, using the discrete wavelet transform implemented by a filter bank. Although the algorithm is developed based on the standard Kalman filtering scheme, the nature of blockwise filtering results in a smoothing-equivalent effect. However, the interpolated filtering produces decomposed estimate output in real-time.     

Two-dimensional linear prediction and spectral estimation on apolar raster

A zero-mean homogeneous random field is defined on a discrete polar raster. Given sample values inside a disk of finite radius, the authors wish to estimate the field's power spectral density using linear prediction. Issues arising include estimation of covariance lags and extendibility of a finite set of lag estimates into a positive semidefinite covariance extension (required for a meaningful spectral density). The authors give a generalized autocorrelation procedure that guarantees positive semidefinite covariance estimates. It first interpolates the data using Gaussians, computes its Radon transform, and applies familiar 1D techniques to each slice. Some numerical examples are provided to justify the validity of the proposed procedure. The authors also propose a correlation-matching covariance extension procedure that uses the Radon transform to extend a given set of covariance lags to the entire plane, when this is possible, and discuss circumstances for which this is impossible     

基于小波变换的MFSK/MQAM信号类内识别法

提出了一种基于小波变换的FSK/MQAM信号类内调制方式识别的改进算法,该方法通过深入分析MFSK和MQAM信号在小波域的不同特征,充分利用小波变换模值的奇异性信息,通过对该信息的有效检测,实现MFSK信号类内识别,并将其推广应用到16QAM和64QAM的分类。对该方法的适应性进行了理论分析和计算机仿真,结果表明,新算法大大提高了MFSK/MQAM信号的识别正确率,并有效降低了识别的信噪比门限。     

基于余弦窗变换的MPSK和QAM信号的码速率估计

电子对抗的实际应用中为了完成信号的分类、解调任务，需要精确估计截获信号的码速率。针对数字通信中常见的MPSK、QAM信号，提出了一种基于余弦窗变换的码速率估计新算法。利用余弦窗变换确定信号相位跳变位置，进而利用谱分析精确估计码速率。理论分析证明了算法的有效性。仿真试验表明，当信噪比大于5dB时算法是有效的，且优于采用小波变换的方法。     

小波包去噪在循环平稳信号DOA估计中的应用

基于信号循环平稳特性的到达方向(DOA)估计算法在噪声成分过大或噪声本身具有循环平稳特性的情况下,性能会迅速下降,为提高算法的估计性能,提出了利用小波包去噪与广义谱相关子空间算法相结合的方法估计信号DOA,通过仿真分析证明该方法提高了估计精度,降低了估计误差,达到了与理论分析相一致的目的.     

Maximum-Likelihood Seismic Deconvolution

The purpose of this paper is to describe a broad spectrum of seismic deconvolution problems and solutions which we refer to collectively as maximum-likelihood (seismic) deconvolution (MLD). Our objective is to perform deconvolution and wavelet estimation for the case of nonminimum phase wavelets. Our approach is to exploit state-variable technology, maximum-likelihood estimation, and a sparse spike train (Bernoulli-Gaussian) model for the reflection signal. Our solution requires detection of significant reflectors, wavelet and variance identification (nonlinear optimization), and estimation of the spike density parameter.     

基于离散小波分解和频率脊线分析的CPM信号符号速率盲估计

非合作接收条件下,连续相位调制（CPM,Continuous Phase Modulation）信号多变未知的信号体制使其符号速率盲估计一直是分析该类信号的难点之一.现有的算法大多直接基于信号的瞬时频率或循环平稳性,存在抗噪性能差,不适用于多指数CPM信号等问题.针对该问题,本文通过分析小波变换在信号分解和时频分析中的优势,提出一种综合利用离散小波（DWT,Discrete Wavelet Transform）分解和频率脊线提取的CPM信号符号速率估计的新算法.算法对比分析表明,所提算法具有更好的抗噪性能且在小数据量时也能达到较好的估计性能.     

Phase estimation using the bispectrum

The information which is of importance in the reflection seismic method resides in the reflectivity series. In order to extract this information about the subsurface, the blurring effect of the seismic wavelet must first be removed. Since this signature is generally unknown, various wavelet estimation schemes have been developed. The one most currently used in the seismic industry is based on the assumption that the seismic wavelet has the minimum-phase property. This restrictive assumption is often incorrect. The purpose of this paper is to explore the use of the bispectrum in order to obviate the minimum-phase requirement. Specifically, using synthetic examples, we develop and compare three different algorithms which determine the wavelet phase from the bispectrum of the reflection seismogram. An important aspect of the problem not treated before is the application of the bispectral technique to band-limited data.     

MPSK信号符号率估计和同步算法

在非协作式通信系统中,符号率估计是参数估计的重要环节,它对调制方式的识别及信号的正确解调有着重要意义.基于haar小波变换分析现有符号率估计算法,针对多进制数字相位调制(MPSK)信号的多速率估计,提出一种多同步参考点的符号率估计算法.该算法通过分析小波的降噪特性,先对接收信号进行小波分解降噪处理,有效地抑制噪声,再选取和修正多同步参考点,使得符号率估计有更高的精度和抗噪性能.仿真验证了提出的算法在CNR＜0时符号率估计的准确率和精度都比传统算法高.     

三阶多项式相位信号参数估计

三阶多项式相位信号常用于描述复杂运动目标的雷达回波,但其未知参数多,参数估计实现起来比较困难。针对此问题,本文提出了一种分两步的三阶多项式相位信号参数估计方法:首先利用Radon变换提取三阶多项式相位信号在三次相位变换后对应的直线,从而得到相位参数的初始值估计;然后将初始值代入最大似然估计函数并利用单纯形法对其进行优化得到精确估计结果。给出了单分量和多分量情况下的参数估计流程。仿真结果表明,本文算法估计性能达到CRB界,且起始工作信噪比门限低。      

一种基于小波域的指纹图像增强算法

面对采集的指纹图像质量较差的问题,提出一种在小波域对指纹图像滤波增强算法。该算法在小波域进行Ga-bor滤波增强,通过Radon变换后的投影估计Gabor滤波的方向和频率,对滤波增强后的子图进行小波重构得到最后的结果,有效地抑制高频扰动对指纹图像质量的影响,提高运算速度和准确性。通过对FVC2000指纹库中的部分低质量指纹图像进行增强,表明该算法对指纹图像的增强效果明显,并且处理速度较快。     

Noninvasive estimation of motor unit conduction velocity distribution using linear electrode arrays

Determining the conduction velocity of motor unit action potentials is one of the most important problems in surface electromyography. The estimate of one average conduction velocity value depends on a variety of uncontrollable factors. More meaningful information is obtained from the estimation of the distribution of the different delays in the myoelectric signals. A solution to the problem is the separation and characterization of the individual components propagating at different velocities. A technique, based on surface electrode array recording, is proposed to estimate motor unit conduction velocity distribution. The method consists in the identification of the single action potentials in the time scale domain (with the continuous wavelet transform) and in the estimation of their conduction velocities based on the beamforming algorithm. The performances of the technique have been evaluated using simulated and real myoelectric signals. The results demonstrate that the technique is accurate and reliable. The method may be useful for the diagnosis of neuromuscular disorders, for the monitoring of muscle fatigue and for noninvasive investigation of individual motor units.     

Two-channel nonseparable wavelets statistically matched to 2-D images

This paper presents a new approach for the estimation of 2-channel nonseparable wavelets matched to images in the statistical sense. To estimate a matched wavelet system, first, we estimate the analysis wavelet filter of a 2-channel nonseparable filterbank using the minimum mean square error (MMSE) criterion. The MMSE criterion requires statistical characterization of the given image. Because wavelet basis expansion behaves as Karhunen-Loève type expansion for fractional Brownian processes, we assume that the given image belongs to a 1st order or a 2nd order isotropic fractional Brownian field (IFBF). Next, we present a method for the design of a 2-channel two-dimensional finite-impulse response (FIR) biorthogonal perfect reconstruction filterbank (PRFB) leading to the estimation of a compactly supported statistically matched wavelet. The important contribution of the paper lies in the fact that all filters are estimated from the given image itself. Several design examples are presented using the proposed theory. Because matched wavelets will have better energy compaction, performance of estimated wavelets is evaluated by computing the transform coding gain. It is seen that nonseparable matched wavelets give better coding gain as compared to nonseparable non-matched orthogonal and biorthogonal wavelets.     

基于小波去噪的T-ray图像复原

针对实时THz脉冲（T-ray）成像系统所成图像分辨率低、受l/f相关噪声干扰严重的特点,提出一种新的基于小波去噪的T—ray图像复原算法。对T-ray图像进行离散小波变换后,先利用广义交叉确认估计出各个分辨率层的噪声闽值,然后对每个分辨率层的高频子带进行迭代去噪,最后对去噪后的T-ray图像采用Jasson-Van-Cittert算法进行复原处理以提高分辨率。实验结果表明,该方法在提高T-ray图像分辨率的同时,能显著地抑制THz成像系统的l/f相关噪声。创新之处在于以广义交叉确认作为T-ray图像中l/f噪声的估计方法,大幅度提高了图像信噪比（-5dB）,避免了噪声带来的复原算法中的不适定问题,达到较好的图像复原效果。     

Seismic wavelet estimation: a frequency domain solution to ageophysical noisy input-output problem

In seismic reflection prospecting for oil and gas a key step is the ability to estimate the seismic wavelet (impulse response) traveling through the Earth. Such estimation enables filters to be designed to deblur the recorded seismic time series and allows the integration of “downhole” and surface seismic data for seismic interpretation purposes. An appropriate model for the seismic time series is a noisy-input/noisy-output linear model. The authors tackle the estimation of the impulse response in the frequency domain by estimating its frequency response function. They use a novel approach where multiple coherence analysis is applied to the replicated observed output series to estimate the output signal-to-noise ratio (SNR) at each frequency. This, combined with an estimate of the ordinary coherence between observed input and observed output, and with the spectrum of the observed input and cross-spectrum of the observed input and output, enables estimation of the frequency response function. The methodology is seen to work well on real and synthetic data