基于多宇宙并行量子遗传算法的非线性盲源分离算法研究

在系统分析非线性盲源分离模型和算法的基础上,提出了基于输出信号联合累积量的非线性盲 源分离算法,并提出采用多宇宙并行量子遗传算法的优化求解方法,仿真结果表明了算法的有效性。     

基于稀疏超指数盲均衡的高频外辐射源雷达发射信号提取

基于天波雷达发射信号的外辐射源雷达需要在接收站提取发射信号用于匹配滤波处理, 而到达接收站的发射信号往往受到电离层反射与折射多径污染的问题, 提出了一种基于超指数与常数模盲均衡算法的发射信号混合盲反卷积方法.利用电离层折射与反射等多径杂波的稀疏性, 采用稀疏处理降低混合算法中超指数盲均衡算法的计算量, 从而实现发射信号的恢复.针对发射信号恢复质量对检测性能的影响进行了分析评估.计算机仿真表明, 所提出的盲均衡算法保持了超指数算法快速收敛的优点, 同时, 在性能损失很小的情况下计算量显著下降, 具有良好的工程应用前景.     

单通道带通信号的复基带盲解卷积算法

该文提出了一种基于多途信道单通道接收的带通数据(波束或传感器输出)自相关函数的盲解卷积算法。该算法先通过复解调将带通信号频谱搬移到0频率附近,然后进行低通滤波和降采样率,得到其复基带信号。再针对单通道复基带信号,以其自相关函数在零延时之外某区间内的实部平方和最小为准则,推导出复基带多途信号盲解卷积的LMS自适应迭代算法。该方法能够适用于带通高斯信号和非高斯信号,与基于高阶统计量的方法相比,对源信号概率分布具有较宽的适用面,计算机仿真结果验证了该方法的正确性。     

基于非连续多帧平滑的卷积混合盲分离频域算法

本文研究了卷积混合盲分离频域算法问题,基于短时傅立叶变换中各帧"部分卷积"的性质,提出了一种非连续多帧平滑的方法.该方法有助于降低源信号"短时谱"的瞬时混合和卷积混合信号的短时谱之间误差,从而提升分离性能.仿真实验证实了提出算法的有效性.     

基于空时频输出矩阵的盲波束形成算法

针对现有的卷积盲源分离方法（如时频分析方法）在宽带信号的分析中计算量较大的问题,提出一种在分数阶傅里叶域滤波基础上的盲波束形成算法。算法首先对接收到的信号作分数阶傅里叶域的峰值滤波,然后计算空时频输出矩阵,最后提出一种信号来向未知的空间盲波束形成算法。该算法充分利用线性调频信号在分数阶傅里叶域能量聚集的特性提高输出信噪比,并减少了运算量。仿真结果表明,算法能够实现宽带信号的盲源分离,且输出性能较之时频方法有一定提高。     

盲解卷积与窄带阵列模型结合的信号分离算法

在水声制导技术中,提出了一种高效分离算法,实现了对多目标源信号的分离,为系统后端对多目标定位提供了技术支持。窄带信号条件下,把盲分离与阵列信号处理结合起来．借助阵列模型把接收的混合信号变成解析信号,然后利用瞬时复值盲分离算法进行分离获得源信号的解析信号,取实部后便是实源信号。从而将实数的卷积混合转化为复数的瞬时混合,在盲分离阵列模型的基础上,通过复数盲分离的手段完成盲解卷积。解卷积恢复的多目标源信号十分有利于多目标特征识别与定位。通过构建正弦信号盲解卷积仿真实验,对文中提出的盲解卷积方法进行了验证。结果表明了该方法的正确性。     

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.     

鸡尾酒会问题及其在军事侦察中应用的研究

分析了鸡尾酒会效应的特点，结合军事侦察的特点，提出了类鸡尾酒会军事侦察系统，并针对这一系统的关键技术盲源分离技术进行了研究。采用空间时频分布盲源分离算法，实现了非平稳信源的盲分离；采用基于自然梯度的多通道盲反卷积算法，实现了卷积QAM信号的解混。仿真结果表明了此种算法在军事侦察的可行性。     

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。      

一种无源双基地雷达直达波恢复方法研究

在山地及城市附近的应用中,严重的多径杂波造成基于调频广播的无源双基地雷达直达波信号恢复困难。文中提出了采用基于空间分集的常数模盲均衡算法,利用空间分集的多通道,实现信号衰落的补偿,而利用常数模盲均衡算法,实现恒模的调频广播信号的反卷积运算,从而完成了对多径的抑制,获得高质量的直达波信号。计算机仿真表明：与无分集的常数模盲均衡算法相比,所提算法获得了更好的目标检测性能。     

Blind deconvolution of spotted image based on slice selection of third-order moment

The 1D blind deconvolution algorithm using maximum time delay slice of the third-order moment ((MTDS-TOM) [Lu, W]) is extended to 2D blind deconvolution for spotted image deblurring. A scaled and shifted version of the image is obtained using a special slice selected from its third-order moment, which is estimated using a 4D blind deconvolution. An application of the proposed method for removing the optical blur of a microarray image is given.     

Multiple frame projection based blind deconvolution

Important a priori information available for blind deconvolution in problems such as astronomical imaging and remote sensing is the information in the multiple frame images in which the object is common for each frame but the point spread function varies. A projection based blind deconvolution algorithm for solving the multiple frame case is proposed     

On the use of Lyapunov criteria to analyze the convergence of blinddeconvolution algorithms

We present an approach to determine sufficient conditions for the global convergence of iterative blind deconvolution algorithms using finite impulse response (FIR) deconvolution filters. The novel technique, which incorporates Lyapunov's direct method, is general, flexible, and can be easily adapted to analyze the behavior of many types of nonlinear iterative signal processing algorithms. Specifically, we find sufficient conditions to guarantee a unique solution for the NAS-RIF algorithm used for blind image restoration. We determine that in many cases, there exists a tradeoff between the quality of the deconvolution result and the uniqueness of the solution. A procedure to determine the length of the deconvolution filter to guarantee a unique solution is established     

An improved fast blind deconvolution algorithm based on decorrelation and block matrix1

In order to alleviate the shortcomings of most blind deconvolution algorithms, this paper proposes an improved fast algorithm for blind deconvolution based on decorrelation technique and broadband block matrix. Althougth the original algorithm can overcome the shortcomings of current blind deconvolution algorithms, it has a constraint that the number of the source signals must be less than that of the channels. The improved algorithm deletes this constraint by using decorrelation technique. Besides, the improved algorithm raises the separation speed in terms of improving the computing methods of the output signal matrix. Simulation results demonstrate the validation and fast separation of the improved algorithm.     

Blind deconvolution of images using optimal sparse representations.

The relative Newton algorithm, previously proposed for quasi-maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smooth approximation of the absolute value is used as the nonlinear term for sparse sources. In addition, we propose a method of sparsification, which allows blind deconvolution of arbitrary sources, and show how to find optimal sparsifying transformations by supervised learning.     

AN IMPROVED FAST BLIND DECONVOLUTION ALGORITHM BASED ON DECORRELATION AND BLOCK MATRIX

In order to alleviate the shortcomings of most blind deconvolution algorithms,this paper proposes an improved fast algorithm for blind deconvolution based on decorrelation technique and broadband block matrix. Althougth the original algorithm can overcome the shortcomings of current blind deconvolution algorithms,it has a constraint that the number of the source signals must be less than that of the channels. The improved algorithm deletes this constraint by using decorrelation technique. Besides,the improved algorithm raises the separation speed in terms of improving the computing methods of the output signal matrix. Simulation results demonstrate the validation and fast separation of the improved algorithm.     

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.