复杂电磁环境下的信号盲分离方法

介绍了基于盲源分离的信号处理基本思想,阐述了ICA,SOBI,AMUSE三种典型算法的原理、特点,通过仿真实验研究了这三种盲源分离算法对于常见波形信号和真实信号的分离性能及其抗噪能力,验证了算法的有效性和可行性。三种方法对于抑制干扰信号,进行信号的有效提取具有实际意义和使用价值。     

瞬时混合盲源分离和最优波束形成的关系研究

最优波束形成和盲源分离是近几十年迅速发展起来的两种重要阵列信号处理方法,在瞬时混合情况下它们具有相似的信号模型,所以本文研究了二者之间的关系。指出,当不存在噪声时,多数盲源分离算法构造的是最优波束形成;当存在噪声时,可以利用盲源分离算法估计的信号导向向量设计最优波束形成。计算机仿真实验验证了这一结论。     

基于核独立成分分析的舰船光尾流信号提取

舰船尾流的激光探测是一种新的鱼雷制导手段。水体的后向散射光信号是舰船尾流后向散射光信号检测的常见干扰,由于其在频域十分接近且信号强度大于尾流信号,因此难以用传统方法提取有用的尾流信号。针对这一问题,提出了一种基于盲源分离的处理方法,将核独立成分分析技术应用于舰船尾流后向散射光信号的提取。介绍了核独立成分分析的基本原理和具体算法,进行了仿真计算,并与传统独立成分分析算法进行比较。结果表明在盲源信号分离中,基于核空间的独立成分分析与其他独立成分分析算法相比更具有准确性。最后应用该方法对海上实验数据进行处理,提取出了舰船尾流信号,取得了良好的效果,验证了该算法的有效性。     

基于最大信噪比测量两相流速度

为了克服噪声对信号的影响,提出一种利用最大信噪比和相关法测量两相流速度的方法.基于最大信噪比的信号分离方法是一种盲源信号分离方法,该算法利用统计独立信号完全分离时信噪比最大作为分离准则,它具有非常低的计算复杂度.这里首先利用盲源信号分离方法分别提取出上游和下游两相流信号,并据此求出两相流信号的相关函数曲线,由此求出信号的渡越时间,最后给出仿真实验的处理结果.实验结果表明该方法能够满足两相流速度的测量要求.     

一种基于人工蜂群算法的混沌信号盲分离方法

混沌信号所固有的非周期、宽带频谱和对初值极度敏感等特性使得对这类信号进行盲分离极为困难. 针对这一问题, 提出一种新的盲分离方法, 该方法通过相空间重构来构造代价函数, 将混沌信号的盲分离转化为一个无约束优化问题, 并利用人工蜂群算法进行求解. 不同于现有的独立成分分析方法仅使用混合信号的统计特性来解决分离问题, 该方法能充分利用混合信号内在的动态特性, 因而在处理混沌信号这种确定性信号时能获得更好的分离效果. 此外, 正交矩阵的参数化表示有效地降低了盲分离问题的复杂性, 使优化过程能快速收敛. 实验结果表明, 该方法具有较快的收敛速度和较高的数值精度, 在分离混沌信号时其整体性能优于现有的几种盲分离方法. 同时, 在分离混沌-高斯混合信号的实验中该方法也展现出优异良好的性能, 这表明该文的方法有应用潜力.     

一种适用于微弱信号提取的盲源分离算法

本文首次提出了适用于微弱信号提取的盲源分离算法,这种方法是在常用的自然梯度串行更新算法基础上采用了有监督的机制。通过观察两个信号互相关的程度自动判别激活函数的种类,实现了超高斯亚高斯强弱信号混合下的盲源分离。信号仿真表明,有监督的盲源分离技术收敛速度较快,精度较高。     

EEMD与EFICA在单通道复合故障诊断中的应用

针对转子不对中和滚动轴承微弱损伤的复合故障诊断问题,提出了一种基于平均经验算法(Ensemble Empirical Mode Decomposition,EEMD)和高效快速独立分量分析(Efficient Variant of FastICA,EFICA)的盲源分离故障诊断方法。利用EEMD算法将单通路复合故障信号分解成多个不同信号特征的本征模函数(Intrinsic Mode Function,IMF),解决了盲源分离中的欠定问题。在此基础上利用EFICA算法对各个不同信号特征的IMF进行故障特征分离。通过仿真实验和转子实验台的实验结果,表明该算法可以有效分离出各个不同的故障特征。     

噪声背景下双输入时延混合系统的盲源分离

噪声背景下应用盲分离技术恢复源信号是盲信号处理的难点之一,本文主要研究了双输入时延有噪混合模型的盲分离方法,和传统的盲分离算法相比,该方法可以有效地利用多阵元的观测信号,对加性噪声具有相当的抑制作用。     

含噪混合数据中相关源信号的盲分离

本文在假设时间白噪声的基础上,通过对阵列施加一个较弱的旋转不变约束提出了一种针对相关源信号盲分离的方法。与现有方法相比,该方法不需要额外的源信号约束条件和噪声自相关矩阵的知识,因此具有较广的使用范围和较强的稳健性。另外,该方法较低的计算复杂度易于其工程实现。计算机仿真结果表明,本文提出的方法对于相关源信号的盲分离具有较好的效果。     

用独立成份分析算法实现水声信号盲分离

独立成份分析算法是在研究信号盲分离过程中出现的一种新方法,本文试图将几种独立成份分析算法用于分离水声信号。分析和比较了５种算法的性能,并用仿真信号对算法进行了仿真,阐述了独立成份分析算法分离水声信号的不足。针对含噪声模型,提出了一种基于独立成份分析算法成功分离水声信号的方法。     

一种基于模式识别的多路盲语音提取方法

盲分离算法能在缺少混合系统参数的条件下仅由观测信号估计初始源,但分离信号存在固有的排列模糊性,这往往导致两次批处理过程中同一信号"对不准",因此很难获得连续的源信号。本文针对盲声源分离中存在的相同问题,根据语音和其他音频信号的特征差异,提出一种修正的自相关函数并以其值作为一个特征基元来表征声音信号的时序相关特性,同时用平均声门波形状参数作为另一个特征基元来表征语音产生的生理效应。以这两个参数作为识别不同音频信号的二维模式特征,采用一种模糊聚类算法提取多路盲分离语音。本方法有效克服了批处理盲声源分离中的信号排列顺序的不确定性,并通过选择合适的阈值提取多路连续语音。仿真给出了5路混合音频信号中盲提取两路连续语音的实验结果。     

A Gabor subband decomposition ICA and MRF hybrid algorithm for infrared image reconstruction from subpixel shifted sequences

An image blind reconstruction, as a blind source separation problem, has been solved recently by independent component analysis (ICA). Based on ICA theory, in this paper, a high resolution image is reconstructed from low resolution and subpixel shifted sequences captured by infrared microscan imaging system. The algorithm has the attractive feature that neither the prior knowledge of the blur kernel nor the value of subpixel misregistrations between the input channels is required. The statistical independence in the image domain is improved by the multiscale Gabor subband decompositions, which are designed for the best ability to cover the whole spatial frequency and to avoid overlapping between the subbands. The mutual information is employed to locate a subband with the least dependent components. In terms of MAP estimator, we combine the super-Gaussian with Markov random field to form a hybrid image distribution. This strategy helps to estimate the separating matrix reasonable to extract the sources with the image properties, that is, sharp enough as well as correlative in local area. The proposed algorithm is capable of performing high resolution image sources which are not strictly independent, and its viability is proved by the computer simulations and real experiments.     

混沌信号在无线传感器网络中的盲分离

混沌信号在本质上属于非线性非高斯信号,它在无线传感器网络下的应用还涉及到信号量化问题,这使得混沌信号在此应用环境下的信号盲分离更为棘手.针对此问题,本文在容积卡尔曼粒子滤波的框架下提出一种解决方法.文中首先推导出观测信号的概率密度函数,在量化比特有限的情况下,采用最优量化器,获得最优的量化结果.在此基础上,使用容积卡尔曼滤波器产生粒子滤波中的重要性概率密度函数,融入最新的观测值,提高粒子对系统状态后验概率的逼近,提高信号盲分离的精度.仿真结果表明算法能够有效地分离混合混沌信号,参数估计的精度及其运算量均优于已有的无先导卡尔曼粒子滤波算法,其运行时间为无先导卡尔曼粒子滤波算法的88.77%.     

基于双传声器对的多声源二维定位跟踪算法

提出一种房间混响声场环境下的多声源二维定位跟踪算法。研究了基于盲源分离的时延估计,以及联合空间分布的多个传声器对的定位算法。用高斯似然函数解决在多源、多维情况下声源定位的时延匹对模糊问题,使之能够用双传声器对实现对多个声源的二维定位,结合粒子滤波算法实现对多个运动声源的跟踪。仿真实验验证了提出算法的有效性。      

Blind speech source separation via nonlinear time-frequency masking

Aim at the underdetermined convolutive mixture model, a blind speech source separation method based on nonlinear time-frequency masking was proposed, where the approximate W-disjoint orthogonality （W-DO） property among independent speech signals in time-frequency domain is utilized. In this method, the observation mixture signal from multimicrophones is normalized to be independent of frequency in the time-frequency domain at first, then the dynamic clustering algorithm is adopted to obtain the active source information in each time-frequency slot, a nonlinear function via deflection angle from the cluster center is selected for time-frequency masking, finally the blind separation of mixture speech signals can be achieved by inverse STFT （short-time Fourier transformation）. This method can not only solve the problem of frequency permutation which may be met in most classic frequency-domain blind separation techniques, but also suppress the spatial direction diffusion of the separation matrix. The simulation results demonstrate that the proposed separation method is better than the typical BLUES method, the signal-noise-ratio gain （SNRG） increases 1.58 dB averagely.     

宽带电磁图像卷积神经网络盲恢复方法研究

在利用抛物反射面对电磁干扰源成像过程中,由于系统衍射受限及成像频带较宽,导致干扰源成像模糊,分辨率低,难以分辨,不同频率不同区域干扰源所成图像分辨率不同,采用已有超分辨算法难以提高分辨率。为了实现宽带电磁图像的盲复原, 应用卷积神经网络的方法。网络训练是直接输入模糊图像,不假设任何特定的模糊和噪声模型情况下,重建出高质量图像。实验和仿真结果证明了卷积神经网络盲恢复方法在宽频带不同成像区域下表现了优于其他盲恢复算法的优势。     

Uncertain evidence and nonlocality

After the work of Suppes and Zanotti it is clear that the proof of the impossibility of local theories is a probability argument. The notion of locality is essentially a principle of conditional statistical independence which is strictly tied to that of exchangeability. De Finetti's celebrated representation theorem makes the connection clear. The way in which Bell's experiment is performed suggests that the probability function which is more suitable to describe it is not exchangeable, but partially exchangeable. It is known that partially exchangeable probability functions show a nonlocal behavior. Working with these functions, it is possible to make use of observations regarding one stochastic process in order to change the distribution of another process. We enlarge to uncertain evidence a classical probability function we have used in deriving some quantum correlations. By means of this enlargement we give simple examples of a nonlocal probability function.     

Gradient method for blind chaotic signal separation based on proliferation exponent

A new method to perform blind separation of chaotic signals is articulated in this paper, which takes advantage of the underlying features in the phase space for identifying various chaotic sources. Without incorporating any prior information about the source equations, the proposed algorithm can not only separate the mixed signals in just a few iterations, but also outperforms the fast independent component analysis(FastICA) method when noise contamination is considerable.     

Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy

This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhance the population’s diversity and ergodicity. Then, inspired by the sigmoid function, a new parameter is designed to strengthen the ability of the algorithm to coordinate early exploration and late development. Finally, the particle swarm optimization learning strategy is introduced into the seagull position updating method to improve the ability of the algorithm to jump out of local optimization. Through the simulation comparison with other algorithms on 12 benchmark test functions from different angles, the experimental results show that SPSOA is superior to other algorithms in stability, convergence accuracy, and speed. In engineering applications, SPSOA is applied to blind source separation of mixed images. The experimental results show that SPSOA can successfully realize the blind source separation of noisy mixed images and achieve higher separation performance than the compared algorithms.