ICA及其在阵列信号处理中的应用研究

独立成分分析（Independent Component Analysis,ICA）是一种有效的盲信号分离（Blind Source Separation,BSS）方法。当目标源信号相互独立时,它能从多通道的混合观测信号中将目标源信号分解开来。本文通过对ICA的详细介绍,对比了ICA模型和阵列信号处理模型的特点,分析了二者的关系,并就二者的综合应用进行了研究,同时对盲波束形成、雷达信号分选和外辐射源雷达信号分离等典型应用进行了介绍和评价。     

基于ICA的无线电通信干扰抑制

抑制无线电通信信号中的干扰是提高通信可靠性的有效措施.传统的干扰抑制技术存在算法、设备复杂,对信号有损伤和实时性差等问题.针对存在的问题,在分析研究盲源分离(BSS)的理论基础上建立了基于最大信噪比算法的ICA通信干扰抑制模型;基于该模型仿真实现了无线电通信信号的分离提取和多路干扰信号的抑制.仿真结果表明,基于ICA的...     

一种适用于微弱信号盲提取的白化方法

独立分量分析(ICA)算法是解决盲信号分离(BSS)问题的最有效方法之一.ICA中,对观测信号预白化处理的作用至关重要.通常采用主分量分析(PCA)来进行预白化处理.实际中,在利用广播、电视等作为照射源的被动雷达系统中,观测信号通常被强噪声和干扰严重污染,这很大程度上降低了BSS方法的性能.然而,传统的BSS方法中没有...     

最大似然估计的独立分量分析法

独立分量分析(ICA)在国内尚属一门新型的方法,它的主要应用是盲信号分离(BSS)。该文首先给出了ICA中一种有效的方法--最大似然估计法(ML),其次分析了ML方法和信息最大化方法(infomax)的等价性,最后给出了相应的计算机仿真结果。     

基于ICA的快速定点算法

介绍了独立分量分析(ICA)的模型定义、数学原理等基本问题.在分析ICA基础上引入了固定点算法(FastICA).FastICA算法收敛速度快,迭代次数由传统算法的2 000次减少到3～10次.实验表明,FsatICA具有良好的盲源分离性能和鲁棒性.     

盲源分离中信号独立性的讨论

在盲源分离(BSS)算法中,对分离信号独立性的衡量直接影响算法的有效性和健壮性,是一个算法成功与否的关键因素。总结了衡量分离信号间独立性大小的一般原则,即从非高斯极大、互信息最小及非线性不相关等角度来衡量。提出了另一种衡量信号独立性的方法以及相关优化函数,拓展了BSS研究的一般思路。     

基于蚁群算法的改进ICA算法

针对FastICA算法存在依赖非线性函数选取的缺陷,为了提高分离结果的可靠性,提出一种基于蚁群算法的改进ICA算法.该算法对非线性函数没有特殊要求,以负熵近似表达式为目标函数,利用蚁群算法代替FastICA算法中的牛顿梯度法,求出最优分离矩阵B,从而对混合信号中的独立分量进行分离.仿真结果验证了改进ICA算法的有效性和...     

基于MUSIC算法的二次雷达应答信号分离方法

为了提高在高密度信号环境下对二次监视雷达(SSR)应答信号的接收性能,该文提出一种将信源数估计和信号到达方向(DOA)估计相结合构建分离矩阵实现交叠信号分离的算法。首先根据交叠信号量测的特征值分布来确定交叠信号的个数;然后利用MUSIC算法作谱峰搜索得到各信号的DOA,并重构混合矩阵;最后通过计算混合矩阵的广义逆得到分离矩阵,并实现对交叠信号的分离。以6阵元均匀线阵为前提进行仿真分析,结果表明所提分离算法可达到90%以上的分离成功率,分离性能和独立成分分析(ICA)算法相当,优于基于投影技术分离算法(PA),但计算量远小于ICA算法,不足ICA算法计算量1/10,更易于工程化应用。     

一种基于非线性函数优化选择的ICA算法

针对超高斯混合信号的分离算法中评价函数的计算复杂度较高的不足,提出了一种使用非线性有理函数进行优化的盲信号分离(BSS)算法,通过仿真验证了新算法在取得较高分离精度的同时,具有较小的计算复杂度.     

基于独立成分分析的被动红外光谱弱信号检测

简要阐述了独立成分分析(independent component analysis,ICA)的基本模型及其假设、含混性、非高斯性度量和通用求解过程,介绍了一种基于峰度的快速ICA算法。提出了基于基本ICA模型的从被动遥感红外光谱中分离出弱目标信号的信号检测方法。实验结果表明：基于ICA的信号提取方法可不依赖于预先采集的“干净”背景光谱,并且与差谱法的结果进行了对比。     

稀疏分量分析在欠定盲源分离问题中的研究进展及应用

伴随着国内外对盲源分离问题研究的日益深入,在独立分量分析等经典算法之外逐步发展出了许多新的算法.稀疏分量分析就是其中有效的方法之一,它利用信号的稀疏分解,克服了独立分量分析非欠定性的要求,解决了欠定情况下的盲源分离问题.本文将以稀疏分量分析为主要对象,归纳总结了近期的研究进展.     

ICA-based and second-order separability of nonlinear models involving reference signals: General properties and application to quantum bits

Relatively few results have been reported about the separability of given classes of nonlinear mixtures by means of statistical criteria such as ICA. We here first prove the ICA separability of a wide class of nonlinear global (i.e. mixing+separating) models involving “reference signals”, i.e. unmixed signals. We also show the second-order separability of sub-classes of the above class of models. This work therefore concerns nonlinear extensions of (linear) adaptive noise cancellation. We illustrate the usefulness of our general results by applying them to a quantum information processing problem, which involves a model of Heisenberg-coupled quantum states (i.e. qubits). This paper opens the way to practical ICA-based and second-order blind source separation (BSS) methods for nonlinear mixtures encountered in various applications. These BSS methods are also outlined in this paper.     

结合ICA和复数神经网络的二麦阵列盲源分离技术

为了降低语音信号盲源分离算法的延时,提高其准确性和稳定性,本文结合传统盲源分离技术和深度神经网络的优势,提出了一种基于ICA独立分量分析和复数神经网络的二麦阵列盲源分离技术。本文将复数递归神经网络和独立分量分析方法有机融合,提出一种基于时频域的双通道复数神经网络,同时解决了独立分量分析中的排列问题。所提方法利输入混合信号利用复数域神经网络计算初始化分离矩阵,神经网络输出采用复数域形式,利用复数学习标签估计复数矩阵,然后采用独立分量分析方法获得目标分离矩阵。实验数据表明,所提方法相较于其它独立分量分析方法提高了盲源分离的实时性和准确性。      

盲信号分离的现状和展望

盲信号分离是近几年才发展起来，用于解决从混合观测数据中分离源信号的一门新技术，已在许多领域获得了广泛应用。本文介绍了盲分离的主要理论和两大类实现方法——独立分量分析和非线性主分量分析，并在此基础上介绍了实现盲信号分离的不同算法、在非线性混合情况下的算法以及盲信号分离将来的发展方向。     

Temporal BYY learning for state space approach, hidden Markovmodel, and blind source separation

The temporal Bayesian Yang-Yang (TBYY) learning has been presented for signal modeling in a general state space approach, which provides not only a unified point of view on the Kalman filter, hidden Markov model (HMM), independent component analysis (ICA), and blind source separation (BSS) with extensions, but also further advances on these studies, including a higher order HMM, independent HMM for binary BSS, temporal ICA (TICA), and temporal factor analysis for real BSS without and with noise. Adaptive algorithms are developed for implementation and criteria are provided for selecting an appropriate number of states or sources. Moreover, theorems are given on the conditions for source separation by linear and nonlinear TICA. Particularly, it has been shown that not only non-Gaussian but also Gaussian sources can also be separated by TICA via exploring temporal dependence. Experiments are also demonstrated     

ICA of Noisy Music Audio Mixtures Based on Iterative Shrinkage Denoising and FastICA Using Rational Nonlinearities

Blind source separation (BSS) has an extensive application prospect in many fields, and independent component analysis (ICA) is a very effective tool for solving the BSS problem. Noisy BSS/ICA, as it approaches the reality, is frequently considered in many practical applications. In this paper, we mainly discuss the “sensor” noise, adding Gaussian white noise to the music audio mixtures. To solve noisy BSS/ICA problem, we deploy denoising pre-processing before performing FastICA. Rather than traditional wavelet shrinkage, we employ a more advanced shrinkage denoising algorithm, parallel coordinate descent (PCD) iterative shrinkage based on redundant dictionary, to accomplish the denoising task. Since the classical nonlinearities (tanh and gauss) used in FastICA are not the optimal ones due to their slow computational speed, we propose two novel rational nonlinearities that have faster computational speed and almost the same or better separation performance comparing with the classical ones. As they originate from Pade approximant of tanh and gauss, but the coefficients are adjusted, we name them Variant Tanh Pade (VTP) and Variant Gauss Pade (VGP), respectively.     

Testing for stochastic independence: application to blind source separation

In this paper, we address the issue of testing for stochastic independence and its application as a guide to selecting the standard independent component analysis (ICA) algorithms when solving blind source separation (BSS) problems. Our investigation focuses on the problem of establishing tests for the quality of separation among recovered sources obtained by ICA algorithms in an unsupervised environment. We review existing tests and propose two contingency table-based algorithms. The first procedure is based on the measure of goodness-of-fit of the observed signals to the model of independence provided by the power-divergence (PD) family of test statistics. We provide conditions that guarantee the validity of the independence test when the individual sources are nonstationary. When the sources exhibit significant time dependence, we show how to adopt Hotelling's T/sup 2/ test statistic for zero mean to create an accurate test of independence. Experimental results obtained from a variety of synthetic and real-life benchmark data sets confirm the success of the PD-based test when the individual source samples preserve the so-called constant cell probability assumption as well as the validity of the T/sup 2/-based test for sources with significant time dependence.     

Extracting Epileptic Feature Spikes Using Independent Component Analysis

In recent years, blind source separation （BSS） by independent component analysis （ICA） has been drawing much attention because of its potential applications in signal processing such as in speech recognition systems, telecommunication and medical signal processing. In this paper, two algorithms of independent component analysis （fixed-point IC,4 and natural gradient-flexible ICA） are adopted to extract human epileptic feature spikes from interferential signals. Experiment results show that epileptic spikes can be extracted from noise successfully. The kurtosis of the epileptic component signal separated is much better than that of other noisy signals. It shows that ICA is an effective tool to extract epileptic spikes from patients＇ electroencephalogram EEG and shows promising application to assist physicians to diagnose epilepsy and estimate the epileptogenic region in clinic.     

联合非高斯性和非线性自相关的有噪盲源分离算法

现有的盲源分离算法往往利用信号某一方面的统计特性来分离信号,例如:利用信号的非高斯特性,或者利用信号的时序特性。在实际应用中,信号往往是具有这两种特性信号的混合,采用信号某一方面的特性往往不能够成功的分离出信号。现有的盲源分离算法往往不考虑噪声的影响,但在实际应用中,噪声的影响是不可避免的。当源信号具有非高斯性和非线性自相关特性时,提出了联合非高斯性和非线性自相关特性的有噪盲源分离算法。计算机仿真表明了提出算法的有效性,和现有的基于非高斯性和非线性自相关特性的有噪盲源分离算法相比,提出算法具有更好的信号分离性能。