Multiscale Wiener filter for the restoration of fractal signals:wavelet filter bank approach

A filter bank design based on orthonormal wavelets and equipped with a multiscale Wiener filter is proposed in this paper for signal restoration of 1/f family of fractal signals which are distorted by the transmission channel and corrupted by external noise. First, the fractal signal transmission process is transformed via the analysis filter bank into multiscale convolution subsystems in time-scale domain based on orthonormal wavelets. Some nonstationary properties, e.g., self-similarity, long-term dependency of fractal signals are attenuated in each subband by wavelet multiresolution decomposition so that the Wiener filter bank can be applied to estimate the multiscale input signals. Then the estimated multiscale input signals are synthesized to obtain the estimated input signal. Some simulation examples are given for testing the performance of the proposed algorithm. With this multiscale analysis/synthesis design via the technique of the wavelet filter bank, the multiscale Wiener filter can be applied to treat the signal restoration problem for nonstationary 1/f fractal signals     

基于多尺度Wiener滤波器的分形噪声滤波

针对淹没在1/f噪声中的有用信号恢复问题,本文提出了一套基于双正交小波变换与Wiener滤波的多尺度滤波算法,并设计出多尺度Wiener滤波器.首先,利用双正交小波变换将带有1/f噪声的信号分解成多尺度的子带信号,通过小波变换对1/f噪声的白化作用,消除了1/f噪声的非平稳性、自相似性和长程相关性.其次,在小波域内,利用Wiener滤波,实现了噪声和有用信号的分离,估计出了各子带中的有用信号.最后,利用双正交小波的精确重构性,较好地恢复出淹没在1/f噪声中的有用信号.仿真实验表明,该滤波器能有效的抑制分形噪声,显著地提高信噪比.     

CFE条件下基于循环维纳滤波的单通道分离算法

在航天测控数传（C＆T）信号中,频谱混叠的现象非常普遍,因此对频谱严重混叠的信号进行单通道盲分离成为信号处理领域中研究的热点和难点。基于线性-共轭-线性频移（LCL-FRESH）滤波的基本概念,考虑到在实际非合作通信应用中基于循环平稳的LCL-FRESH滤波依赖于较高的循环频率精度,然而循环频率误差（CFE）总是无可避免,提出了一种改进的CFE校正算法,简单分析了误差条件下滤波器性能下降的原因。仿真表明,所研究的算法可以有效分离存在CFE下时频重叠的数传通信信号。     

一种新的宽带信号正交分量的解调方法

在D通道均匀DFT分析滤波器组基础上给出了一种新的信号正交分量的解调结构 ,为解调宽带信号 (B fc)的正交分量 ,系统采用两组D通道均匀DFT分析滤波器组通带互补方法 ,以克服单组滤波器中滤波器的非理想性所造成的信号失真。在频域中当信号频谱处于两个或更多个滤波器通道时 ,通过频谱组合办法直接得到实信号的正半谱或负半谱。在载波频率已恢复的情况下给出了IQ正交分量的解调算法。仿真证明该系统结构和算法的正确性和高效性     

Estimation of fractal signals using wavelets and filter banks

A filter bank design based on orthonormal wavelets and equipped with a multiscale Wiener filter was recently proposed for signal restoration and for signal smoothing of 1/f family of fractal signals corrupted by external noise. The conclusions obtained in these papers are based on the following simplificative hypotheses: (1) The wavelet transformation is a whitening filter, and (2) the approximation term of the wavelet expansion can be avoided when the number of octaves in the multiresolution analysis is large enough. In this paper, we show that the estimation of 1/f processes in noise can be improved avoiding these two hypotheses. Explicit expressions of the mean-square error are given, and numerical comparisons with previous results are shown     

Simple coherent receivers for partial response continuous phasemodulation

By using a pulse-amplitude-modulation representation of binary continuous-phase-modulation signals, the authors develops a novel optimum Viterbi sequence detector and a near-optimum Viterbi receiver with low complexity. For modulation index 0.5, where a linear receiver can be used, a minimum-mean-squared-error linear receiver filter is derived. The performance of all of these is analyzed, using the Gaussian minimum-shift-keying signal (GMSK) for illustration. It is shown that a GMSK receiver consisting of two matched filters and a four-state Viterbi algorithm performs with less than 0.24-dB degradation compared with the optimal receiver. The linear receiver is optimum for all values of E _b/N₀ (bit-energy-to-noise one-sided spectral density ratio). A design method for its filter is given. The filter is equivalent to a cascade of a matched filter and a Wiener filter estimator. Both upper and lower bounds for the bit-error probability are calculated. Simulation results which confirm the analysis are given     

Approximate Time-Variable Coherence Analysis of Multichannel Signals

We present a new method for signal extraction from noisy multichannel epileptic seizure onset EEG signals. These signals are non-stationary which makes time-invariant filtering unsuitable. The new method assumes a signal model and performs denoising by filtering the signal of each channel using a time-variable filter which is an estimate of the Wiener filter. The approximate Wiener filters are obtained using the time-frequency coherence functions between all channel pairs, and a fix-point algorithm. We estimate the coherence functions using the multiple window method, after which the fix-point algorithm is applied. Simulations indicate that this method improves upon its restriction to assumed stationary signals for realistically non-stationary data, in terms of mean square error, and we show that it can also be used for time-frequency representation of noisy multichannel signals. The method was applied to two epileptic seizure onset signals, and it turned out that the most informative output of the method are the filters themselves studied in the time-frequency domain. They seem to reveal hidden features of the epileptic signal which are otherwise invisible. This algorithm can be used as preprocessing for seizure onset EEG signals prior to time-frequency representation and manual or algorithmic pattern classification.     

小波变换域的局部自适应Wiener滤波器设计方法研究

小波阈值去噪方法被广泛地用在信号去噪中,这个方法在很多信号空间上是近似最优的。但在MSE意义上最优的信号估计是Wiener滤波器,鉴于传统小波变换域Wiener滤波器的缺点,本文设计了小波域局部自适应Wiener滤波器。实验仿真验证本方法具有较好的去噪效果。     

Minimax estimation of unknown deterministic signals in colorednoise

The estimation of a deterministic signal corrupted by random noise is considered. The strategy is to find a linear noncausal estimator which minimizes the maximum mean square error over an a priori set of signals. This signal set is specified in terms of frequency/energy constraints via the discrete Fourier transform. Exact filter expressions are given for the case of additive white noise. For the case of additive colored noise possessing a continuous power spectral density, a suboptimal filter is derived whose asymptotic performance is optimal. Asymptotic expressions for the minimax estimator error are developed for both cases. The minimax filter is applied to random data and is shown to solve asymptotically a certain worst-case Wiener filter problem     

Iterative and sequential algorithms for multisensor signalenhancement

In problems of enhancing a desired signal in the presence of noise, multiple sensor measurements will typically have components from both the signal and the noise sources. When the systems that couple the signal and the noise to the sensors are unknown, the problem becomes one of joint signal estimation and system identification. The authors specifically consider the two-sensor signal enhancement problem in which the desired signal is modeled as a Gaussian autoregressive (AR) process, the noise is modeled as a white Gaussian process, and the coupling systems are modeled as linear time-invariant finite impulse response (FIR) filters. The main approach consists of modeling the observed signals as outputs of a stochastic dynamic linear system, and the authors apply the estimate-maximize (EM) algorithm for jointly estimating the desired signal, the coupling systems, and the unknown signal and noise spectral parameters. The resulting algorithm can be viewed as the time-domain version of the frequency-domain approach of Feder et al. (1989), where instead of the noncausal frequency-domain Wiener filter, the Kalman smoother is used. This approach leads naturally to a sequential/adaptive algorithm by replacing the Kalman smoother with the Kalman filter, and in place of successive iterations on each data block, the algorithm proceeds sequentially through the data with exponential weighting applied to allow adaption to nonstationary changes in the structure of the data. A computationally efficient implementation of the algorithm is developed. An expression for the log-likelihood gradient based on the Kalman smoother/filter output is also developed and used to incorporate efficient gradient-based algorithms in the estimation process     

一种自适应语音增强均衡器的研究

在参考滤波器组的基础上，提出一个用于语音和音频信号进行时不变或自适应谱修正的数字滤波器结构，主要用于音频信号均衡和降噪。在频域计算滤波器系数的同时，信号在时域滤波。与通常频域处理相比，在信号延迟、原型滤波器设计、复杂性等方面，该结构有良好的特性。该算法既适用于均匀频率分辨率，也适用于非均匀频率分辨率。     

Time-frequency plane Wiener filtering of the high-resolution ECG:background and time-frequency representations

This paper introduces the concept of a posteriori Wiener filtering (APWF), performed in the time-frequency plane. The objective is to improve the signal-to-noise ratio (SNR) of the ensemble-averaged high-resolution electrocardiogram (HRECG). APWF was developed to address the problem of a limited ensemble size for estimating ensemble-averaged evoked potentials. For the HRECG, the authors identify the major challenge as adapting the time-frequency structure of the filter to that of low-level cardiac signals. Technical limitations and the characteristics of HRECG signals make time-frequency analysis of the ensemble average problematic. Normal and abnormal signal components are difficult to distinguish due to low time-frequency energy concentration and limited spectrotemporal resolution. However, considering the entire ensemble of repetitive ECG records, signal and noise components are separable in the time-frequency plane. This forms the basis of the new time-frequency plane Wiener (TFPW) filter, applicable to any ensemble averaging problem involving repetitive deterministic signals mixed with uncorrelated noise     

Order statistic filter banks

Filter banks play a major role in multirate signal processing where these have been successfully used in a variety of applications. In the past, filter banks have been developed within the framework of linear filters. It is well known, however, that linear filters may have less than satisfactory performance whenever the underlying processes are non-Gaussian. We introduce the nonlinear class of order statistic (OS) filter banks that exploit the spectral characteristics of the input signal as well as its rank-ordering structure. The attained subband signals provide frequency and rank information in a localized time interval. OS filter banks can lead to significant gains over linear filter banks, particularly when the input signals contain abrupt changes and details, as is common with image and video signals. OS filter banks are formed using traditional linear filter banks as fundamental building blocks. It is shown that OS filter banks subsume linear filter banks and that the latter are obtained by simple linear transformations of the former. To illustrate the properties of OS filter banks, we develop simulations showing that the learning characteristics of the LMS algorithm, which are used to optimize the weight taps of OS filters, can be significantly improved by performing the adaptation in the OS subband domain.     

A polynomial approach to optimal and adaptive filtering withapplication to speech

Explicit polynomial solutions to the Wiener filtering problem are given. They rely on the identification of innovations models for the disturbance and for the noisy signal. The Wiener filter is found from the solution of a diophantine equation. Results illustrating the attenuation of background interference in a speech signal are presented. The explicit approach presented does not rely on minimizing a prediction error over a performance surface and can be applied where two input techniques are impracticable or impossible     

Time-frequency plane Wiener filtering of the high-resolution ECG:development and application

The time-frequency plane Wiener (TFPW) filter is a new method, based on a posteriori Wiener filtering principles, to enhance the performance of ensemble averaging. This paper develops the mathematical aspects of the TFPW filter, and assesses its performance with elementary signals, such as sine waves and chirps, and authentic high-resolution electrocardiogram (HRECG) ensembles. The principal feature of the TFPW filter is its use of the time-frequency plane to accommodate signal nonstationarity. Using a posteriori computed statistics of the ensemble, the filter matches itself to the time-frequency structure of the signal to be estimated. The method is sufficiently general to be applicable to any class of repetitive signal with a deterministic time-frequency structure and additive noise in the ensemble. It is concluded that significant improvements in both estimated signal fidelity and noise reduction are possible with the TFPW filter, compared to conventional ensemble averaging     

T2 restoration and noise suppression of hybrid MR images using Wiener and linear prediction techniques

The authors address the problem of enhancing hybrid magnetic resonance (MR) images degraded by T2 effects and additive measurement noise. To reduce imaging time, MR signals are acquired using hybrid imaging (HI) sequences such as rapid acquisition relaxation-enhanced (RARE) and fast spin-echo (FSE). With these techniques, T2 effects act as a distortion filter. This T2 filter affects the signal and results in image spatial resolution and/or contrast loss. Furthermore, the amplitude and phase discontinuities in the T2 filter frequency response function may generate serious ringing artifacts. These distortions will damage image quality and affect object detectability. The authors use the Wiener filter and linear prediction (LP) technique to process HI MR signals in the spatial frequency domain (K-space) and the hybrid domain, respectively. Based on the average amplitude symmetry constraint of the spin echo signal, the amplitude frequency response function of the T2 distortion filter can be estimated and used in the Wiener filter for a global T2 amplitude restoration. Then, the linear prediction technique is utilized to obtain the local signal amplitude and phase estimates around the discontinuities of the frequency response function of the T2 filter. These estimates are used to make local amplitude and phase corrections. The effectiveness of this combined technique in correcting T2 distortion and reducing the measurement noise is analyzed and demonstrated using experiments on both phantoms and human studies.     

Optimal constrained representation and filtering of signals

A random signal is observed in independent random noise. We are addressing the problem of finding the optimum signal estimate that is constrained to lie in a given linear subspace. The optimality is defined in the sense of weighted mean square error. In the second step, we find the optimum linear subspace of given dimensionality. It is shown to be the linear manifold spanned by the eigenvectors of the simultaneous diagonalization of the signal and noise covariance, that correspond to the largest eigenvalues. The result is valid for both discrete and continuous time. For large observation time and stationary signals, it is shown that the constrained optimal estimate is determined by the two spectral densities and a weighted Fourier Transform of the noise observations. The above result applies to both discrete time and continuous time signals.The Wiener filter emerges as a special case of the constrained filtering estimate, when the linear subspace is enlarged to coincide with the total measurement space.     

A transform domain SVD filter for suppression of muscle noise artefacts in exercise ECG's

The proposed filter assumes the noisy electrocardiography (ECG) to be modeled as a signal of deterministic nature, corrupted by additive muscle noise artefact. The muscle noise component is treated to be stationary with known second-order characteristics. Since noise-free ECG is shown to possess a narrow-band structure in discrete cosine transform (DCT) domain and the second-order statistical properties of the additive noise component is preserved due to the orthogonality property of DCT, noise abatement is easily accomplished via subspace decomposition in the transform domain. The subspace decomposition is performed using singular value decomposition (SVD). The order of the transform domain SVD filter required to achieve the desired degree of noise abatement is compared to that of a suboptimal Wiener filter using DCT. Since the Wiener filter assumes both the signal and noise structures to be statistical, with a priori known second-order characteristics, it yields a biased estimate of the ECG beat as compared to the SVD filter for a given value of mean-square error (mse). The filter order required for performing the subspace smoothing is shown to exceed a certain minimal value for which the mse profile of the SVD filter follows the minimum-mean-quare error (mmse) performance warranted by the suboptimal Wiener filter. The effective filter order required for reproducing clinically significant features in the noisy ECG is then set by an upper bound derived by means of a finite precision linear perturbation model. A significant advantage resulting from the application of the proposed SVD filter lies in its ability to perform noise suppression independently on a single lead ECG record with only a limited number of data samples.     

Optimal time-frequency deconvolution filter design fornonstationary signal transmission through a fading channel: AF filterbank approach

The purpose of this paper is to develop a new approach-time-frequency deconvolution filter-to optimally reconstruct the nonstationary (or time-varying) signals that are transmitted through a multipath fading and noisy channel. A deconvolution filter based on an ambiguity function (AF) filter bank is proposed to solve this problem via a three-stage filter bank. First, the signal is transformed via an AF analysis filter bank so that the nonstationary (or time-varying) component is removed from each subband of the signal. Then, a Wiener filter bank is developed to remove the effect of channel fading and noise to obtain the optimal estimation of the ambiguity function of the transmitted signal in the time-frequency domain. Finally, the estimated ambiguity function of the transmitted signal in each subband is sent through an AF synthesis filter bank to reconstruct the transmitted signal. In this study, the channel noise may be time-varying or nonstationary. Therefore, the optimal separation problem of multicomponent nonstationary signals is also solved by neglecting the transmission channel     

A multistage representation of the Wiener filter based onorthogonal projections

The Wiener filter is analyzed for stationary complex Gaussian signals from an information theoretic point of view. A dual-port analysis of the Wiener filter leads to a decomposition based on orthogonal projections and results in a new multistage method for implementing the Wiener filter using a nested chain of scalar Wiener filters. This new representation of the Wiener filter provides the capability to perform an information-theoretic analysis of previous, basis-dependent, reduced-rank Wiener filters. This analysis demonstrates that the cross-spectral metric is optimal in the sense that it maximizes mutual information between the observed and desired processes. A new reduced-rank Wiener filter is developed based on this new structure which evolves a basis using successive projections of the desired signal onto orthogonal, lower dimensional subspaces. The performance is evaluated using a comparative computer analysis model and it is demonstrated that the low-complexity multistage reduced-rank Wiener filter is capable of outperforming the more complex eigendecomposition-based methods