共查询到18条相似文献,搜索用时 171 毫秒
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基于ICA的雷达信号欠定盲分离算法 总被引:2,自引:0,他引:2
该文针对源信号时域和频域不充分稀疏的情况,提出了欠定盲源分离中估计混合矩阵的一种新方法。该方法对等间隔分段的观测信号应用独立分量分析(ICA)的盲分离算法获得多个子混合矩阵,然后对其分选剔除了不属于原混合矩阵的元素,最后利用C均值聚类的学习算法获得对混合矩阵的精确估计,解决了源信号在时域和频域不充分稀疏的情况下准确估计混合矩阵的问题。在估计出混合矩阵的基础上,利用基于稀疏分解的统计量算法分离出源信号。由仿真结果,以及与传统的K均值聚类,时域检索平均算法对比的实验结果说明了该文算法的有效性和鲁棒性。 相似文献
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基于时频分析的混合矩阵估计方法 总被引:1,自引:0,他引:1
在盲源分离信号处理中,尤其在欠定条件下(观测信号数目大于源信号数目),精确的估计混合矩阵是具有挑战性的问题。现存部分方法利用信号的稀疏性进行求解,并假设在时域或者时频域中源信号不重叠,然而这类方法在假设条件不满足,即源信号部分重叠情况下随着信号稀疏性降低性能恶化明显。本文针对具有较弱稀疏性的源信号,提出了一种基于时频分析的欠定盲源分离的混合矩阵估计方法。首先,利用源信号时频变换后系数实部与虚部比值的差异性选择单源点;其次,运用经典的聚类方法估计解混合矩阵的各向量。仿真结果表明:提出的方法简易可行并具有较好的估计性能。 相似文献
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基于EMD和ICA的单通道语音盲源分离算法 总被引:1,自引:0,他引:1
针对单通道语音信号盲分离的问题,结合盲源分离和经验模式分解的优点.提出了一种基于经验模式分解的单通道语音信号源数估计和盲源分离方法。对语音混合信号进行经验模式分解,利用贝叶斯算法估计语音源数目,根据源信号数目重组多通道语音混合信号,并采用独立分量分析实现语音信号的盲分离。仿真实验表明,使用此法能有效地估计通道语音信号源数和分离盲源。 相似文献
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欠定盲源分离问题中基于源信号稀疏性的两阶段法中,混合矩阵估计的准确与否,直接影响源信号的恢复效果。文中提出了一种在稀疏域估计混合矩阵的新方法。该方法通过搜索稀疏域中同一直线附近的点,利用这些点重构出混合矩阵,避免了远离直线周边的点对估计混合矩阵的干扰,从而大大降低了计算量。仿真表明该算法性能良好。 相似文献
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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。 相似文献
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混合矩阵的估计是稀疏源盲分离的关键组成部分,其估计精度直接影响到源信号的估计精度.本文首先针对K-means聚类算法依赖初始值选取的问题,将微分进化算法思想引入到K-means聚类算法中,提出了一种改进的K-means聚类算法.利用该算法,对稀疏源混合信号数据进行聚类,保证了聚类结果的鲁棒性.然后利用霍夫变换,对每一类数据的聚类中心进行修正,从而估计出混合矩阵,提高了混合矩阵的估计精度.仿真实验表明,相比于经典的稀疏源混合矩阵盲估计算法,本文算法具有更强的鲁棒性和更高的估计精度. 相似文献
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针对传统盲分离混合矩阵估计鲁棒性差、易受初始值影响、精度不高等问题,该文将人工蜂群算法(ABC)用到盲分离中,结合稀疏信号混合矩阵估计的特点,提出一种基于不同搜索策略和编码方式的两阶段蜂群算法的混合矩阵估计方法,通过新的蜜蜂搜索行为和子蜂群之间的协同作业,明显加快了算法的收敛速度,提高了混合矩阵的估计精度。仿真实验表明,该方法在源个数较多、弱稀疏、低信噪比的情况下仍然可以很好地估计混合矩阵。相比已有方法,该方法不仅具有很强的鲁棒性和很高的估计精度,而且不需要太大的计算量。 相似文献
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基于非负矩阵分解算法进行盲信号分离 总被引:1,自引:1,他引:0
独立分量分析(ICA)已被广泛运用于线性混合模型的盲源分离问题,但却有两个重要的限制:信源统计独立和信源非高斯分布。然而更有意义的线性混合模型是:观测信号是非负信源的非负线性混合,信源之间可以统计相关且可以为高斯分布。本文针对盲源分离问题,提出了一种运用新近国际上提出的一种非负矩阵分解算法(NMF算法)进行统计相关信源的盲源分离方法,该方法没有信源统计独立和信源非高斯分布的限制,只要信源之间没有一阶原点统计相关,则可很好实现对信源的分离。大量仿真及与传统ICA进行盲源分离的比较,验证了运用NMF进行包括统计相关信源和高斯分布信源的盲源分离的可行性和有效性。 相似文献
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Probability estimation for recoverability analysis of blind source separation based on sparse representation 总被引:6,自引:0,他引:6
Li Y. Amari S.-I. Cichocki A. Guan C. 《IEEE transactions on information theory / Professional Technical Group on Information Theory》2006,52(7):3139-3152
An important application of sparse representation is underdetermined blind source separation (BSS), where the number of sources is greater than the number of observations. Within the stochastic framework, this paper discusses recoverability of underdetermined BSS based on a two-stage sparse representation approach. The two-stage approach is effective when the source matrix is sufficiently sparse. The first stage of the two-stage approach is to estimate the mixing matrix, and the second is to estimate the source matrix by minimizing the 1-norms of the source vectors subject to some constraints. After estimating the mixing matrix and fixing the number of nonzero entries of a source vector, we estimate the recoverability probability (i.e., the probability that the source vector can be recovered). A general case is then considered where the number of nonzero entries of the source vector is fixed and the mixing matrix is drawn from a specific probability distribution. The corresponding probability estimate on recoverability is also obtained. Based on this result, we further estimate the recoverability probability when the sources are also drawn from a distribution (e.g., Laplacian distribution). These probability estimates not only reflect the relationship between the recoverability and sparseness of sources, but also indicate the overall performance and confidence of the two-stage sparse representation approach for solving BSS problems. Several simulation results have demonstrated the validity of the probability estimation approach. 相似文献
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Underdetermined blind source separation based on sparse representation 总被引:14,自引:0,他引:14
Yuanqing Li Amari S. Cichocki A. Ho D.W.C. Shengli Xie 《Signal Processing, IEEE Transactions on》2006,54(2):423-437
This paper discusses underdetermined (i.e., with more sources than sensors) blind source separation (BSS) using a two-stage sparse representation approach. The first challenging task of this approach is to estimate precisely the unknown mixing matrix. In this paper, an algorithm for estimating the mixing matrix that can be viewed as an extension of the DUET and the TIFROM methods is first developed. Standard clustering algorithms (e.g., K-means method) also can be used for estimating the mixing matrix if the sources are sufficiently sparse. Compared with the DUET, the TIFROM methods, and standard clustering algorithms, with the authors' proposed method, a broader class of problems can be solved, because the required key condition on sparsity of the sources can be considerably relaxed. The second task of the two-stage approach is to estimate the source matrix using a standard linear programming algorithm. Another main contribution of the work described in this paper is the development of a recoverability analysis. After extending the results in , a necessary and sufficient condition for recoverability of a source vector is obtained. Based on this condition and various types of source sparsity, several probability inequalities and probability estimates for the recoverability issue are established. Finally, simulation results that illustrate the effectiveness of the theoretical results are presented. 相似文献
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Blind source separation for convolutive mixtures based on the joint diagonalization of power spectral density matrices 总被引:1,自引:0,他引:1
Tiemin Mei Alfred Mertins Fuliang Yin Jiangtao Xi Joe F. Chicharo 《Signal processing》2008,88(8):1990-2007
This paper studies the problem of blind separation of convolutively mixed source signals on the basis of the joint diagonalization (JD) of power spectral density matrices (PSDMs) observed at the output of the separation system. Firstly, a general framework of JD-based blind source separation (BSS) is reviewed and summarized. Special emphasis is put on the separability conditions of sources and mixing system. Secondly, the JD-based BSS is generalized to the separation of convolutive mixtures. The definition of a time and frequency dependent characteristic matrix of sources allows us to state the conditions under which the separation of convolutive mixtures is possible. Lastly, a frequency-domain approach is proposed for convolutive mixture separation. The proposed approach exploits objective functions based on a set of PSDMs. These objective functions are defined in the frequency domain, but are jointly optimized with respect to the time-domain coefficients of the unmixing system. The local permutation ambiguity problems, which are inherent to most frequency-domain approaches, are effectively avoided with the proposed algorithm. Simulation results show that the proposed algorithm is valid for the separation of both simulated and real-word recorded convolutive mixtures. 相似文献
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一种频率域的盲源分离算法 总被引:1,自引:1,他引:0
提出了一种频率域基于第二特征函数的窄带信号盲分离算法,理论上证明了本方法能够从有噪观察数据中得到无噪混合矩阵估计。仿真结果表明本方法信号分离性能优于时域方法。在高信噪比时,本方法的分离信号绝对误差和比时域方法低9.5dB。 相似文献
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Blind Decomposition of Transmission Light Microscopic Hyperspectral Cube Using Sparse Representation
《IEEE transactions on medical imaging》2009,28(8):1317-1324
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盲源分离有一个重要假设:源信号最多只含一个高斯信号。否则,基于统计量的盲分离算法性能会恶化。本文从广义矩形分布出发,通过把时域中的一维信号映射到二维的时-频表示来提供信号的频谱内容随时间变化的信息,并对时频谱进行Hough变换处理,利用不同高斯源的时频分布差异性,避开统计量提出了一种能分离多个高斯源的盲分离算法,扩展了盲源分离的应用领域。 相似文献