【摘要】 研究由若干个滑动平均(MA)信号序列叠加形成的多道时间序列的分解与复原问题。首先从信号的独立性出发,利用信号的高阶统计信息,采用独立成分分析(ICA)中的固定点(FixedPoint)算法将混合信号进行分离,然后设计了一种基于高阶统计量的MA模型的自适应辨识算法,算法在每次迭代中先估计MA的阶数,再估计MA的参数,由选用的线性方程组保证了参数的唯一可辨识性。最后通过模拟实验验证了该方法的有效性。更多还原

【Abstract】 Decomposition and recovery on Moving Average(MA)superposition process contained in multivariable time series were researched in this paper.Firstly,because of the independence of the high-order statistical information,the mixtures were separated by searching a reversible matrix with the Independent Component Analysis(ICA)algorithm-fixed point algorithm.Then an adaptive method was designed to identify MA model based on higher-order cumulant.In every iteration,it estimated the order and the parameters of MA.Finally,the effectiveness of the algorithm was verified by the computer simulation.更多还原