Shi Zhenwei, Zhang Changshui
IEEE Trans Neural Netw. 2007 Sep;18(5):1516-24. doi: 10.1109/tnn.2007.895823.
This letter addresses blind (semiblind) source extraction (BSE) problem when a desired source signal has temporal structures, such as linear or nonlinear autocorrelations. Using the temporal characteristics of sources, we develop objective functions based on the generalized autocorrelations of primary sources. Maximizing the objective functions, we propose simple fixed-point source extraction algorithms. We give the stability analysis and prove convergence properties of the algorithms as the generalized autocorrelation function is linear or nonlinear. Especially, as the generalized autocorrelation function is linear, the algorithm has interesting character of "one-iteration" convergence under some conditions. Computer simulations and real-data application experiments show that the algorithms are appealing BSE methods for temporal signals of interest by capturing the linear or nonlinear autocorrelations of the desired sources.
本文探讨当期望源信号具有时间结构（如线性或非线性自相关）时的盲（半盲）源提取（BSE）问题。利用源的时间特性，我们基于主源的广义自相关开发目标函数。通过最大化目标函数，我们提出了简单的定点源提取算法。我们给出了稳定性分析，并证明了算法在广义自相关函数为线性或非线性时的收敛特性。特别地，当广义自相关函数为线性时，该算法在某些条件下具有“一次迭代”收敛的有趣特性。计算机仿真和实际数据应用实验表明，这些算法通过捕捉期望源的线性或非线性自相关，是用于感兴趣时间信号的有吸引力的BSE方法。
