A New Look to Multichannel Blind Image Deconvolution

The aim of this paper is to propose a new look to MBID, examine some known approaches, and provide a new MC method for restoring blurred and noisy images. First, the direct image restoration problem is briefly revisited. Then a new method based on inverse filtering for perfect image restoration in the noiseless case is proposed. The noisy case is addressed by introducing a regularization term into the objective function in order to avoid noise amplification. Second, the filter identification problem is considered in the MC context. A new robust solution to the degradation matrix filter is then derived and used in conjunction with a total variation approach to restore the original image. Simulation results and performance evaluations using recent image quality metrics are provided to assess the effectiveness of the proposed methods.     

多通道盲反卷积算法综述

本文阐述了多通道盲反卷积（Multichannel Blind Deconvolution-MBD）的发展历程、基本模型及假设、数学原理以及通用求解过程,讨论了目前MBD几个研究方向的发展现状与面临的问题。在分析了MBD研究进展的基础上,从时域、频域、时频域、子空间、子带技术及其他算法六个方面分类综述和比较了MBD典型算法的特点与方法思路,说明了各类算法的优缺点以及主要问题。最后指出了目前多通道盲反卷积算法研究存在的不足,并提出了MBD未来理论和应用研究中可继续深入的开放课题。      

基于非参数密度估计的多通道盲解卷积

非参数密度估计方法被用来直接估计在自然梯度盲解郑积算法中遇到的评价函数(score function)。与用一个非线性函数简单地代替评价函数相比较,这种直接估计评价函数的方法的主要优点是:它可以用来对杂系混合信号,即同时包含超高斯和亚高斯的信号,进行盲解卷积。因为评价函数可以被直接的估计出来,因此,就不需要针对不同的源信号选择不同的非线性函数来代替评价函数。这种方法可以用在更加“盲”的情况。     

基于卡尔曼滤波的同步盲系统辨识与解卷积方法

在盲信道均衡或盲语音去混响应用中,盲多信道系统辨识通常是信号解卷积的前提条件,即盲辨识过程后跟一个解卷积过程。本文提出一种基于卡尔曼滤波的同步盲系统辨识与解卷积方法,其中卡尔曼滤波的状态矢量由多信道系统参数与源信号矢量组成,过程方程和测量方程则建立在单输入-多输出系统(SIMO)的输入输出关系及信道间交叉关联关系(Cross Relation)基础上。此外,盲系统辨识部分与解卷积部分是可以解耦的,生成两个看似独立的卡尔曼滤波问题,并且这两个卡尔曼滤波问题可以实现并行计算。与级联结构相比,这种并行结构更有利于算法优化和实时信号处理。仿真表明,对于无噪声理想信号模型,本算法可以实现完全系统辨识和解卷积(信号误差比可达到100 dB以上),说明理论正确;对于实测的混响语音信号亦可以实现一定的去混响效果。      

Geometrical Structures of FIR Manifold and Multichannel Blind Deconvolution

In this paper we study geometrical structures of the manifold of Finite Impulse Response (FIR) filters, and develop a natural gradient learning algorithm for blind deconvolution. First, A Lie group structure is introduced to the FIR manifold and the Riemannian metric is then derived by using the isometric property of the Lie group. The natural gradient on the FIR manifold is obtained by introducing a nonholonomic transformation. The Kullback-Leibler divergence is introduced as the measure of mutual independence of the output signals of the demixing model and a feasible cost function is derived for blind deconvolution. An efficient learning algorithm is presented based on the natural gradient approach and its stability analysis is also provided. Finally, we give computer simulations to demonstrate the performance and effectiveness of the proposed natural gradient algorithm.     

基于不完整约束自然梯度算法的多道盲解卷

针对多输入多输出(MIMO)系统的Bussgang算法可能收敛到错误的解，而且收敛速度慢的缺点。章提出了不完整约束的自然梯度算法，该算法是由不完整约束条件与自然梯度算法的结合而推导出来的。通过计算机仿真对这两种多道盲解卷算法进行了比较，仿真试验表明：提出的算法收敛速度快，并且比Bussgang算法稳定。     

Gradient Adaptive Paraunitary Filter Banks for Spatio-Temporal Subspace Analysis and Multichannel Blind Deconvolution

Paraunitary filter banks are important for several signal processing tasks, including coding, multichannel deconvolution and equalization, adaptive beamforming, and subspace processing. In this paper, we consider the task of adapting the impulse response of a multichannel paraunitary filter bank via gradient ascent or descent on a chosen cost function. Our methods are spatio-temporal generalizations of gradient techniques on the Grassmann and Stiefel manifolds, and we prove that they inherently maintain the paraunitariness of the multichannel adaptive system over time. We then discuss the necessary practical approximations, modifications, and simplifications of the methods for solving two relevant signal processing tasks: (i) spatio-temporal subspace analysis and (ii) multichannel blind deconvolution. Simulations indicate that our methods can provide simple, useful solutions to these important problems.     

盲源分离和盲反卷积

盲信号处理是信号处理领域的热点研究问题,盲源分离和盲反卷积是盲信号处理的重要组成部分近年来取得许多重要进展.本文主要介绍盲源分离和盲反卷积的基本模型、数学原理和研究进展;分析了各种方法的特点并指出了进一步的研究方向.     

基于量子遗传算法的多通道通信信号盲反卷积算法研究

本文在分析多输入多输出盲反卷积的网络结构和算法模型的基础上，提出了一种基于输出信号上下文信息的盲反卷积算法，并提出一种采用量子遗传算法的新的优化求解方法，对仿真的通信信号分离的结果表明算法的有效性。     

一种基于时频分析的语音卷积信号盲分离算法

本文根据语音信号在实际环境下传播存在多径效应而造成反射信号之间产生时延差异的特点,提出了一种新的基于卷积混合矩阵模型的盲分离算法.该算法利用语音信号频谱中普遍存在的稀疏特性,通过对两路接收信号语谱图的对比分析估计出模型中时延参数和幅度系数的待定值;更进一步,从极大似然估计的思想出发,构造了一种基于时延参数和幅度系数的可信度函数,提出了通过寻找上述可信度函数的峰值以准确确定混合矩阵参数的方法.与现有的基于独立性假设的盲分离算法不同,本算法利用了语音信号频谱中普遍存在的稀疏特性,适用于求解大多数场合下的盲源分离问题.由于本算法本质上是一种非迭代的算法,且不存在发散的问题,故具有快速、稳定的特点.仿真实验和实际环境下所得到的实验结果表明,该算法能在各种信噪比的条件下准确地估计出由环境所确定的时延和幅度参数,并据此成功地分离出源语音信号,是一种面向真实环境下语音盲分离应用的有效算法.     

一种新的盲解卷积准则和算法

定义了盲解卷积问题的期望解后 ,将二阶累积量和四阶累积量合并为一个新的统计量 ,称为归一化累积量 ,考察信号通过线性时不变系统时归一化累积量的特性 ,形成一个基于归一化累积量的盲解卷积准则 ;借助于经典的最陡梯度算法 ,导出了一种新的盲均衡算法 ,计算机模拟验证了该算法     

MCA: A Multichannel Approach to SAR Autofocus

We present a new noniterative approach to synthetic aperture radar (SAR) autofocus, termed the multichannel autofocus (MCA) algorithm. The key in the approach is to exploit the multichannel redundancy of the defocusing operation to create a linear subspace, where the unknown perfectly focused image resides, expressed in terms of a known basis formed from the given defocused image. A unique solution for the perfectly focused image is then directly determined through a linear algebraic formulation by invoking an additional image support condition. The MCA approach is found to be computationally efficient and robust and does not require prior assumptions about the SAR scene used in existing methods. In addition, the vector-space formulation of MCA allows sharpness metric optimization to be easily incorporated within the restoration framework as a regularization term. We present experimental results characterizing the performance of MCA in comparison with conventional autofocus methods and discuss the practical implementation of the technique.      

一种微弱直接序列扩频信号的盲自适应解扩算法

针对微弱直扩信号扩频码的盲估计和信息码的盲解扩问题,本文提出了一种能同时分离直扩信号扩频码和信息码的非线性盲自适应恒模算法,达到了对直扩信号盲处理。本文首先提出了直扩信号的盲分离问题,然后详细分析推导了盲自适应随机梯度恒模算法,最后将该盲自适应随机梯度恒模算法应用到了对微弱直扩信号的盲分离中,并从理论上阐明了可以用该算法来实现直扩信号的盲分离。所提出的算法完全不同于以往的基于矩阵分解（奇异值分解、特征分解等）的伪码盲估计方法,它的存储开销量和计算量都比较小,可以实现对较长伪码构造的直扩信号的处理,而且它的计算速度较快,在某种程度上解决了传统的基于矩阵分解的方法在直扩信号的实时处理及实现上的困难。理论分析和数值结果都表明了所提方法能较好地工作在较低的输入信噪比条件下。      

利用幅度谱或多信道信息求解半盲反褶积

本文指出对于一维有限时宽实序列,输入x(n),系统单位样值响应y(n),几乎总能由x(n)(或y(n))的幅度谱和输出z(n)唯一确定。本文进一步指出利用多信道输出信息z_1(n),x(n)及y_1(n)几乎总能唯一被确定,其中i表示第i个信道。文中给出了四个利用已知信息重构有限长能量有限序列x(n),y(n)的唯一性定理。     

基于最小均方误差准则的盲多用户检测新算法

码分多址信号在无线多径衰落信道条件下的盲多用户检测具有很大的理论和实际意义。该文提出了一种直接基于最小均方误差(MMSE)准则的盲多用户检测算法。为保证算法收敛到目标用户,提出了一种基于MMSE准则的新的线性约束方法。该方法能保证检测器收敛到最优MMSE解。此外,该文还设计了一种形式简单、快速收敛的迭代算法。对比现有的最小输出能量(MOE)算法,仿真结果表明本文算法具有较好的性能。     

一种新的盲多用户检测最大似然方法

线性受限最大似然盲多用户检测是盲多用户检测中的一类重要方法,现有的限制方法不能保证目标用户的最优解落在限制的搜索空间中,因此在理论上不能达到最优解。提出了一种新的限制方法,能保证目标用户的最优解落在限制的搜索空间中,因此保证算法收敛到目标用户。与现有方法相比,这里给出的算法结构简单,降低了运算复杂度。仿真试验表明其具有较低的误码率和较高的系统容量。     

Gradient Adaptive Algorithms for Contrast-Based Blind Deconvolution

This paper presents extensions of stochastic gradient independent component analysis (ICA) methods to the blind deconvolution task. Of particular importance in these extensions are the constraints placed on the deconvolution system transfer function. While unit-norm constrained ICA approaches can be directly applied to the prewhitened blind deconvolution task, an allpass filter constraint within the optimization procedure is more appropriate. We show how such constraints can be approximately imposed within gradient adaptive finite-impulse-response (FIR) filter implementations by proper extensions of gradient techniques within the Stiefel manifold of orthonormal matrices. Both on-line time-domain and block-based frequency-domain algorithms are described. Simulations verify the superior performance behaviors provided by our allpass-constrained algorithms over standard unit-norm-constrained ICA algorithms in blind deconvolution tasks.     

基于累积量极值的非线性系统盲反卷积

提出一种新的基于累积量极值的非线性系统盲反卷积算法.通过引入中间变量作为隐含观测量,反卷积系统估计转化为两个子系统估计问题.第一个子系统估计是一具有完备指导信号训练集的后非线性系统(post nonlinear system)辨识,第二个子系统的估计则是一般基于累积量极值的线性系统盲反卷积.与其它算法比较,新算法具有明确的收敛性质和快速的收敛速度.     

信道盲辨识的新方法

只要单输入多输出(SIMO)信道的公因式满足本文提出的充分必要条件,那么本文方法就可以有效地辨识这些带公零点的信道.本文算法由以下步骤组成:先借助整数约束二次优化盲检测属于给定字符集的发送信号,然后根据这些估得的发送信号辨识传输信道.仿真结果表明:本文新算法明显优于现有的文献算法.     

Blind Deconvolution Denoising for Helicopter Vibration Signals

Critical aircraft assets are required to be available when needed, while exhibiting attributes of reliability, robustness, and high confidence under a variety of flight regimes, and maintained on the basis of their current condition rather than on the basis of scheduled maintenance practices. New and innovative technologies must be developed and implemented to address these concerns. Condition-based maintenance requires that the health of critical components/systems be monitored and diagnostic/prognostic strategies be developed to detect and identify incipient failures and predict the failing component's remaining useful life. Typically, vibration and other key indicators onboard an aircraft are severely corrupted by noise, thus curtailing the ability to accurately diagnose and predict failures. This paper introduces a novel blind deconvolution denoising scheme that employs a vibration model in the frequency domain and attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes are defined and data from a helicopter are used to demonstrate the effectiveness of the proposed approach.