测量误差对反卷积影响的分析及其滤波器的设计思想

本文分析了测量误差对反卷积稳定解影响的机理，并以此为根据提出了关于反卷积滤波器的设计思想，其设计公式与已广泛应用的设计公式相当一致。     

一种单脉冲雷达多通道L1正则化波束锐化方法

该文针对单脉冲雷达波束锐化问题,提出一种多通道L1正则化波束锐化方法。首先根据最大后验概率准则推导了适合于单脉冲雷达波束锐化的多通道L1正则化模型,然后提出一种扩展的迭代收缩阈值算法来解决多通道L1正则化问题。理论分析和仿真实验表明,该方法在保证波束锐化性能的同时提高了抑制噪声的能力,有效地解决了单脉冲雷达中各通道方向图不满足强互质条件带来的噪声泄漏问题。其性能要明显优于现有的单脉冲雷达波束锐化方法。     

反褶积分层解释法在铀矿找矿中的应用

文章简要地介绍了反褶积分层解释法的原理及在γ测井物探数据处理和定量解释过程中的应用,说明了其在铀矿找矿中的直观、方便、快捷的特点。     

Continuous-Time Blind Channel Deconvolution Using Laguerre Shifts

The objective of this paper is to study the problem of continuous-time blind deconvolution of a pulse amplitude modulated signal propagated over an unknown channel and perturbed by additive noise. The main idea is to use so-called Laguerre filters to estimate a continuous-time model of the channel. Laguerre-filter-based models can be viewed as an extension of finite-impulse-response (FIR) models to the continuous-time case, and lead to compact and parsimonious linear-in-the-parameters models.? Given an estimate of the channel, different symbol estimation techniques are possible. Here, the shift property of Laguerre filters is used to derive a minimum mean square error estimator to recover the transmitted symbols. This is done in a way that closely resembles recent FIR-based schemes for the corresponding discrete-time case.? The advantage of this concept is that physical a priori information can be incorporated in the model structure, like the transmitter pulse shape. Date received: July 23, 1998. Date revised: August 26, 1999.     

去卷积伏安法中离散数据处理的傅里叶最小二乘法研究

提出了傅里叶最小二乘法对卷积伏安数据的处理，经过参数优化后，处理后的伏安数据可以用于进一步的卷积伏安运算中，处理后的估安波形未发生变形，峰位置准确。     

A unified SVM framework for signal estimation

This paper presents a review in the form of a unified framework for tackling estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The paper formalizes our developments in the area of DSP with SVM principles. The use of SVMs for DSP is already mature, and has gained popularity in recent years due to its advantages over other methods: SVMs are flexible non-linear methods that are intrinsically regularized and work well in low-sample-sized and high-dimensional problems. SVMs can be designed to take into account different noise sources in the formulation and to fuse heterogeneous information sources. Nevertheless, the use of SVMs in estimation problems has been traditionally limited to its mere use as a black-box model. Noting such limitations in the literature, we take advantage of several properties of Mercerʼs kernels and functional analysis to develop a family of SVM methods for estimation in DSP. Three types of signal model equations are analyzed. First, when a specific time-signal structure is assumed to model the underlying system that generated the data, the linear signal model (so-called Primal Signal Model formulation) is first stated and analyzed. Then, non-linear versions of the signal structure can be readily developed by following two different approaches. On the one hand, the signal model equation is written in Reproducing Kernel Hilbert Spaces (RKHS) using the well-known RKHS Signal Model formulation, and Mercerʼs kernels are readily used in SVM non-linear algorithms. On the other hand, in the alternative and not so common Dual Signal Model formulation, a signal expansion is made by using an auxiliary signal model equation given by a non-linear regression of each time instant in the observed time series. These building blocks can be used to generate different novel SVM-based methods for problems of signal estimation, and we deal with several of the most important ones in DSP. We illustrate the usefulness of this methodology by defining SVM algorithms for linear and non-linear system identification, spectral analysis, non-uniform interpolation, sparse deconvolution, and array processing. The performance of the developed SVM methods is compared to standard approaches in all these settings. The experimental results illustrate the generality, simplicity, and capabilities of the proposed SVM framework for DSP.     

Information fusion steady-state white noise deconvolution estimators with time-delayed measurements and colored measurement noises

White noise deconvolution or input white noise estimation problem has important application backgrounds in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the Auto-Regressive Moving Average (ARMA) innovation model, under the linear minimum variance optimal fusion rules, three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises. They can handle the input white noise fused filtering, prediction and smoothing problems. The accuracy of the fusers is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula of computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.     

迭代频域反卷积滤波器的参数分析

本文首先分析了测量误差对反卷积稳定解的影响,然后介绍了几种能有效解决该问题的迭代频域反卷积滤波器,对其滤波函数的选取以及最佳滤波参数的确定展开讨论,并对它们的性能进行评述和比较。     

Wavelet-based noise reduction for improved deconvolution of time-series data in dynamic susceptibility-contrast MRI

Dynamic susceptibility-contrast (DSC) MRI requires deconvolution to retrieve the tissue residue function R(t) and the cerebral blood flow (CBF). In this study, deconvolution of time-series data was performed by wavelet-transform-based denoising combined with the Fourier transform (FT). Traditional FT-based deconvolution of noisy data requires frequency-domain filtering, often leading to excessive smoothing of the recovered signal. In the present approach, only a low degree of regularisation was employed while the major noise reduction was accomplished by wavelet transformation of data and Wiener-like filtering in the wavelet space. After inverse wavelet transform, the estimate of CBF·R(t) was obtained. DSC-MRI signal-versus-time curves (signal-to-noise ratios 40 and 100) were simulated, corresponding to CBF values in the range 10–60 ml/(min 100 g). Three shapes of the tissue residue function were investigated. The technique was also applied to six volunteers. Simulations showed CBF estimates with acceptable accuracy and precision, as well as independence of any time shift between the arterial input function and the tissue concentration curve. The grey-matter to white-matter CBF ratio in volunteers was 2.4±0.2. The proposed wavelet/FT deconvolution is robust and can be implemented into existing perfusion software. CBF maps from healthy volunteers showed high quality.     

Discrete constrained iterative deconvolution algorithms with optimized rate of convergence

In this paper, some new constrained discrete deconvolution algorithms based on an iterative equation are presented. The constraints are—the signal extent (signal support)—the positivity—the level bounds. The algorithms minimize either the error energy or a positive functional. The connections with previous works are studied. An experimental comparison of the algorithms convergence speed is studied with a synthetic sequence to be recovered. The restoration error and both the deconvoluted signal and its spectrum show clearly the performances of the algorithms and their ability to achieve a spectral extrapolation. The deconvolution from noisy data is investigated.