Blind separation algorithm without nonlinear functions

A new blind separation algorithm without nonlinear functions is proposed. The algorithm is derived using the decorrelation process near an equilibrium point. The algorithm uses the correlation between its outputs and the sensor signals in the decorrelation process. The validity and performance of the algorithm are confirmed through computer simulations     

Blind signal separation of linear mixture using trilinear decomposition

This paper introduces a new source separation technique exploiting the time coherence of the source signals. The proposed approach relies only on stationary second order statistics. Blind Signal Separation (BSS) method using trilinear decomposition is proposed in this paper. Simulation results reveal that our proposed algorithm has the better blind signal separation performance than joint di-agonalization method. Our proposed algorithm does not require whitening processing. Moreover, our proposed algorithm works well in the underdetermined condition, where the number of sources exceeds than the number of sensors.     

Blind separation of sources using a new polynomial equation

The authors present a simple method for estimating the mixing matrix in the two source separation problem. It is proven that the separation can be obtained by solving a second-degree polynomial equation that involves fourth-order cumulants     

Blind digital signal separation using successive interferencecancellation iterative least squares

Blind separation of instantaneous linear mixtures of digital signals is a basic problem in communications. When little or nothing can be assumed about the mixing matrix, signal separation may be achieved by exploiting structural properties of the transmitted signals, e.g., finite alphabet or coding constraints. We propose a monotonically convergent and computationally efficient iterative least squares (ILS) blind separation algorithm based on an optimal scaling lemma. The signal estimation step of the proposed algorithm is reminiscent of successive interference cancellation (SIC) ideas. For well-conditioned data and moderate SNR, the proposed SIC-ILS algorithm provides a better performance/complexity tradeoff than competing ILS algorithms. Coupled with blind algebraic digital signal separation methods, SIC-ILS offers a computationally inexpensive true least squares refinement option. We also point out that a widely used ILS finite alphabet blind separation algorithm can exhibit limit cycle behavior     

Blind constant modulus equalization via convex optimization

In this paper, we formulate the problem of blind equalization of constant modulus (CM) signals as a convex optimization problem. The convex formulation is obtained by performing an algebraic transformation on the direct formulation of the CM equalization problem. Using this transformation, the original nonconvex CM equalization formulation is turned into a convex semidefinite program (SDP) that can be efficiently solved using interior point methods. Our SDP formulation is applicable to baud spaced equalization as well as fractionally spaced equalization. Performance analysis shows that the expected distance between the equalizer obtained by the SDP approach and the optimal equalizer in the noise-free case converges to zero exponentially as the signal-to-noise ratio (SNR) increases. In addition, simulations suggest that our method performs better than standard methods while requiring significantly fewer data samples.     

Blind three dimensional deconvolution via convex optimization

Multidimensional Systems and Signal Processing - In this paper we discuss recovering two signals from their convolution in 3 dimensions. One of the signals is assumed to lie in a known subspace and...     

利用时频域稀疏性的LFM信号盲源分离方法

信号分离是雷达电子对抗的重要环节。考虑到雷达信号在时频域具有稀疏性的特点,在独立分量分析的基础上,提出了一种基于时频域稀疏性的线性调频雷达信号盲源分离方法。首先对混合信号进行短时傅里叶变换,在每个频点利用自然梯度算法分离信号,由分离信号幅度的比值作为对源信号后验概率的估计;然后根据相邻频点后验概率序列的相关性进行排序,确保各个频点的分离信号属于同一个源信号;最后设计时频掩码分离信号。进行了线性调频雷达信号卷积混合的盲分离实验,所提方法分离结果明显优于传统独立分量分析方法的分离结果,验证了该方法的有效性。     

Blind separation of superimposed shifted images using parameterized joint diagonalization.

We consider the blind separation of source images from linear mixtures thereof, involving different relative spatial shifts of the sources in each mixture. Such mixtures can be caused, e.g., by the presence of a semi-reflective medium (such as a window glass) across a photographed scene, due to slight movements of the medium (or of the sources) between snapshots. Classical separation approaches assume either a static mixture model or a fully convolutive mixture model, which are, respectively, either under- or over-parameterized for this problem. In this paper, we develop a specially parameterized scheme for approximate joint diagonalization of estimated spectrum matrices, aimed at estimating the succinct set of mixture parameters: the static (gain) coefficients and the shift values. The estimated parameters are, in turn, used for convenient frequency-domain separation. As we demonstrate using both synthetic mixtures and real-life photographs, the advantage of the ability to incorporate spatial shifts is twofold: Not only does it enable separation when such shifts are present, but it also warrants deliberate introduction of such shifts as a simple source of added diversity whenever the static mixing coefficients form a singular matrix-thereby enabling separation in otherwise inseparable scenes.     

应用萤火虫算法的混沌信号盲源分离

针对现有的独立成分分析法分离混合混沌信号精度不理想的问题,提出了一种新的混沌信号盲分离方法。该方法以求解最优解混矩阵为目标,利用峭度构造目标函数,将混沌信号的盲源分离转化为一个优化问题,并用萤火虫算法求解。同时,通过预白化和正交矩阵的参数化表示降低优化问题的维数,能有效提高分离精度。仿真结果表明,无论是处理混合的混沌映射信号还是混合的混沌流信号,该方法都能快速收敛,并且其分离精度在各项实验中都优于独立成分分析法等现有的盲源分离方法。     

单通道混合信号盲分离算法

介绍了单通道混合信号的概念及盲源分离的现状,对实时线性混叠盲分离方法展开研究,分类探讨基于变换域滤波、多参数联合估计、符号序列与信道参数联合估计以及多维映射的单通道混合信号盲分离方法,分析比较各类盲源分离方法的处理对象、前提条件和优缺点,并进行了总结.最后通过仿真实验分析了基于粒子滤波与编码辅助的单通道盲分离方法中粒子数目、编码方式、源信号幅度比和频差等对分离算法性能的影响.     

脉冲噪声中的盲源分离方法

盲源分离的目的在于只利用接收数据把被瞬时线性混合的源信号恢复出来,该文讨论的是一种在复各向同性的SS噪声中的盲源分离方法,SS过程能够很好地描述许多具有冲激特性的信号和噪声,但其二阶和高阶统计量是不存在的,所以首先用基于子空间逼近和白化的方法对观测数据进行处理,然后利用特征矩阵近似联合对角化方法来估计源信号和混合矩阵。仿真结果说明该方法具有良好的性能。     

Blind signal separation: statistical principles

Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis that aim to recover unobserved signals or “sources” from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach, but it requires us to venture beyond familiar second order statistics, The objectives of this paper are to review some of the approaches that have been developed to address this problem, to illustrate how they stem from basic principles, and to show how they relate to each other     

Blind source separation by new M-WARP algorithm

A new independent component analysis technique is presented, which is based on the information-theoretic approach and implemented by the functional-link network, that allows mixed independent sub-Gaussian and super-Gaussian source signals to be separated out. To assess the theory, the results of computer simulations performed both on synthetic and real-world data are presented, and the performances of the new algorithm compared with those exhibited by the `mixture of densities' based algorithm of Xu et al. [1997]     

Blind source separation based on time-frequency signalrepresentations

Blind source separation consists of recovering a set of signals of which only instantaneous linear mixtures are observed. Thus far, this problem has been solved using statistical information available on the source signals. This paper introduces a new blind source separation approach exploiting the difference in the time-frequency (t-f) signatures of the sources to be separated. The approach is based on the diagonalization of a combined set of “spatial t-f distributions”. In contrast to existing techniques, the proposed approach allows the separation of Gaussian sources with identical spectral shape but with different t-f localization properties. The effects of spreading the noise power while localizing the source energy in the t-f domain amounts to increasing the robustness of the proposed approach with respect to noise and, hence, improved performance. Asymptotic performance analysis and numerical simulations are provided     

Blind separation of synchronous co-channel digital signals using anantenna array. I. Algorithms

We propose a maximum-likelihood (ML) approach for separating and estimating multiple synchronous digital signals arriving at an antenna array at a cell site. The spatial response of the array is assumed to be known imprecisely or unknown. We exploit the finite alphabet property of digital signals to simultaneously estimate the array response and the symbol sequence for each signal. Uniqueness of the estimates is established for BPSK signals. We introduce a signal detection technique based on the finite alphabet property that is different from a standard linear combiner. Computationally efficient algorithms for both block and recursive estimation of the signals are presented. This new approach is applicable to an unknown array geometry and propagation environment, which is particularly useful In wireless communication systems. Simulation results demonstrate its promising performance     

Blind separation of piecewise stationary non-Gaussian sources

We address independent component analysis (ICA) of piecewise stationary and non-Gaussian signals and propose a novel ICA algorithm called Block EFICA that is based on this generalized model of signals. The method is a further extension of the popular non-Gaussianity-based FastICA algorithm and of its recently optimized variant called EFICA. In contrast to these methods, Block EFICA is developed to effectively exploit varying distribution of signals, thus, also their varying variance in time (nonstationarity) or, more precisely, in time-intervals (piecewise stationarity). In theory, the accuracy of the method asymptotically approaches Cramér–Rao lower bound (CRLB) under common assumptions when variance of the signals is constant. On the other hand, the performance is practically close to the CRLB even when variance of the signals is changing. This is demonstrated by comparing our algorithm with various methods that are asymptotically efficient within ICA models based either on the non-Gaussianity or the nonstationarity. The benefit of our algorithm is demonstrated by examples with real-world audio signals.     

Blind source separation of signals with known alphabets using /spl epsi/-approximation algorithms

We show that blind separation of signals in given alphabets can be formulated into a quadratic optimization problem with integer constraints. Then, efficient /spl epsi/-approximation algorithms are applied to directly estimate the transmitted signals. The proposed approach does not require any high order statistics. Moreover, the algorithms converge to an /spl epsi/ neighborhood of the global optimum with polynomial computational complexity. Simulations show that the algorithm achieves satisfactory performance using a short length of data.     

Blind separation of synchronous co-channel digital signals using anantenna array. II. Performance analysis

We have recently proposed a blind maximum likelihood approach for demodulating multiple co-channel digital signals received synchronously at an antenna array. This approach exploits the finite alphabet (FA) property of digital signals to simultaneously estimate the array response and symbol sequence for each signal. We have presented two computationally efficient block algorithms for computing the array response and signal estimates: iterative least-squares with projection (ILSP) and iterative least-squares with enumeration (ILSE). In this paper, we study the performance of these algorithms using both analysis and simulation. We derive the probability of error in detecting the signals under the assumption that the array responses are known. We use this probability to estimate the probability of error in the algorithms. Simulation results confirm that the detection error probability yields a good approximation to the performance of the blind algorithms     

Blind separation of instantaneous mixtures of nonstationary sources

Most source separation algorithms are based on a model of stationary sources. However, it is a simple matter to take advantage of possible nonstationarities of the sources to achieve separation. This paper develops novel approaches in this direction based on the principles of maximum likelihood and minimum mutual information. These principles are exploited by efficient algorithms in both the off-line case (via a new joint diagonalization procedure) and in the on-line case (via a Newton-like procedure). Some experiments showing the good performance of our algorithms and evidencing an interesting feature of our methods are presented: their ability to achieve a kind of super-efficiency. The paper concludes with a discussion contrasting separating methods for non-Gaussian and nonstationary models and emphasizing that, as a matter of fact, “what makes the algorithms work” is-strictly speaking-not the nonstationarity itself but rather the property that each realization of the source signals has a time-varying envelope     

基于细菌趋药性算法的盲图像分离方法

提出了一种新的基于细菌趋药性(BC)算法的盲图像分离方法,利用图像信号的规范四阶累积量作为目标函数,使用BC算法对目标函数进行优化以实现图像的盲分离。每分离出一幅图像后,从混合图像中消除该幅图像成分后再进行下一次分离,从而最终实现所有源图像的逐次分离。仿真结果表明,本文算法能够有效实现对多幅混合自然图像的盲分离,且具有较好的分离效果。