引入参考信号的新峭度快速不动点算法

发布时间：2018-03-22 13:07

  本文选题：盲源分离　切入点：独立成分分析　出处：《信号处理》2014年12期 　论文类型：期刊论文

【摘要】：在盲源分离和独立成分分析中,峭度是衡量随机信号非高斯性的常用对比准则,通过不同类型的算法对其进行优化,找到非高斯性极大值点,即实现了源信号的提取或分离。例如,基于峭度的快速不动点算法,它是一种收敛速度很快的算法。最近,Marc Castella等人提出了一类基于所谓"参考信号"的对比准则,以及对应的梯度最大化优化算法,这些算法具有很好的收敛性能。受其启发,文章以一种类似的方式将"参考信号"思想应用到峭度中,得到一种新颖的对比函数,并基于该新峭度对比函数,提出了一种新的快速不动点算法。与经典的基于峭度的快速不动点算法相比,该算法极大地提高了收敛速度,尤其是随着信号样值点数的增加,该算法的优势会更加明显。文章分析和证明了该新峭度对比函数的局部收敛性,给出了新算法的详细推导过程,仿真实验验证了该算法的性能,并与经典算法进行了比较分析。
[Abstract]:In blind source separation and independent component analysis (ICA), kurtosis is a common contrast criterion to measure the non-#china_person0# character of random signals. For example, the fast fixed point algorithm based on kurtosis is a fast convergence algorithm. Recently, Marc Castella et al proposed a kind of comparison criterion based on so-called "reference signal". These algorithms have good convergence performance. Inspired by these algorithms, this paper applies the idea of "reference signal" to kurtosis in a similar way, and obtains a novel contrast function. Based on the new kurtosis contrast function, a new fast fixed point algorithm is proposed. Compared with the classical fast fixed point algorithm based on kurtosis, this algorithm greatly improves the convergence rate, especially with the increase of signal sample points. This paper analyzes and proves the local convergence of the new kurtosis contrast function, gives the detailed derivation process of the new algorithm, and verifies the performance of the algorithm by simulation, and compares it with the classical algorithm.
【作者单位】： 解放军理工大学通信工程学院;国防信息学院;
【基金】：国家自然科学基金资助项目(61172061) 江苏省自然科学基金资助项目(BK2011117)
【分类号】：TN911.7
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本文编号：1648779