共查询到17条相似文献,搜索用时 48 毫秒
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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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基于拉普拉斯势函数的欠定盲分离中源数的估计 总被引:1,自引:0,他引:1
本文提出了一种新的欠定盲源分离中源信号个数的估计算法,利用稀疏混合信号的特征,引入拉普拉斯势函数,并采用聚类算法来估计其局部最大值,由此得到源信号的个数估计.所提出的算法具有较好的抗噪声性能,对信号的稀疏度要求低.仿真实验结果说明了该算法的有效性. 相似文献
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在进行欠定盲分离时,特别是对于源信号数目及混合矩阵动态变化的情况,常规的欠定盲分离及源数估计方法不能对源信号数目的变化时刻做出判断,因此很难实现动态变化的源信号数目实时和准确的估计。针对这个问题,提出了一种动态变化混叠模型下欠定盲源分离中的源数估计方法。首先,建立动态变化混叠情形下盲源分离的数学模型及动态标识矩阵。其次,基于构建的动态标识矩阵统计和判断动态源信号数目的变化情况。最后,通过分段时间内多维观测矢量采样点聚类区间局部峰值统计,实现动态变化混叠模型下盲源分离中的源信号数目的有效估计。仿真结果表明,该方法能有效实现动态变化混叠模型下欠定盲源分离中的源数估计,并且信号估计效果良好。 相似文献
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为了进一步提高欠定盲源分离算法中混合矩阵估计方法的性能,提出了一种基于加权最小二乘支持向量机(SVM)的欠定盲源分离混合矩阵估计方法。该方法利用信号的方向角度特征估计出有效信源信号个数,然后采用加权最小二乘支持向量机方法获得初始权值,每次将其中一个权值对应的样本点作为测试样本,其余点作为训练样本,依次对样本的误差变量进行更新,再根据权值计算公式实现所有权值的更新,进而确定最优分类平面,实现对观测信号的最优分类,最终估计出混合矩阵。实验结果表明,新算法是有效的,其平均误差是基于K-均值方法误差的0.2倍左右,是基于SVM算法平均误差的0.5倍左右。 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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基于源信号数目估计的欠定盲分离 总被引:3,自引:0,他引:3
该文利用欠定盲分离下稀疏源信号的特点,估计源信号的数目且恢复源信号。通常在用两步法来解决欠定盲分离时,首先利用K-均值算法对观测信号聚类估计出混叠矩阵,最后利用最短路径法来恢复源信号,但是在以往的算法中,第1步估计混叠矩阵时,通常假设源信号数目是已知的,从而进行K-均值聚类,而事实上源信号数目根本无法知道,因此对源信号数目的估计对两步法有很重要的影响。因此本文提出了一种新的两步法算法,其中第1步利用稀疏源信号反映在观测信号中的特征来准确地估计出稀疏源信号的数目,且能得到混叠矩阵,从而恢复源信号。最后的仿真结果,以及与通常的K-均值聚类算法对比的仿真结果说明了此算法的可行性和优异的性能。 相似文献
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This paper considers mixing matrix estimation for underdetermined blind source separation. First, we propose an effective detection algorithm to identify single source points where only one source occurs. The detection algorithm finds single source points by utilizing the time–frequency coefficients of mixed signals and the complex conjugates of the coefficients. Then, a method based on probability density is proposed in order to find more reliable single source points and cluster them. Finally, the mixing matrix is obtained through re-selecting and clustering single source points. The experimental results indicate that the algorithm can accurately estimate the mixing matrix when there are fewer sensors than sources. 相似文献
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Fu Weihong Zhou Xinbiao Nong Bin Li Changle Liu Jun 《Wireless Personal Communications》2019,104(4):1283-1300
Wireless Personal Communications - Under the circumstances that source signals are sufficiently sparse, an algorithm based on density measurement for blind estimation of the underdetermined mixing... 相似文献
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A nonparametric density estimation method is used to directly estimate the score
functions encountered in relative gradient (or natural gradient) adaptation algorithms in
the blind source separation problem. Compared to the method where simple nonlinear
functions are used to replace the unknown score functions, the key advantage of the direct
estimation of the score functions lies in the fact that it enables the algorithm to separate
hybrid mixtures of sources that contain both super-Gaussian and sub-Gaussian signals. The
source statistics required for the choices of the nonlinear functions is no longer needed,
because the score functions are directly estimated. The algorithm is thus expected to be
applicable to more blind cases. 相似文献
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Blind source separation attempts to recover independent sources which have been linearly mixed to produce observations. We consider blind source separation with non-stationary mixing, but stationary sources. The linear mixing of the independent sources is modelled as evolving according to a Markov process, and a method for tracking the mixing and simultaneously inferring the sources is presented. Observational noise is included in the model. The technique may be used for online filtering or retrospective smoothing. The tracking of mixtures of temporally correlated is examined and sampling from within a sliding window is shown to be effective for destroying temporal correlations. The method is illustrated with numerical examples. 相似文献