时变混合共轭梯度盲提取算法

A Conjugate Gradient Algorithm of Time-varying Mixtures for Blind Source Extraction

针对传统独立分量分析(ICA)方法对时变信道跟踪能力较差的问题,提出了一种时变混合共轭梯度盲提取算法。该算法有效利用了各源信号的时序结构差异,仅利用其二阶统计量解决了具有不同功率谱密度的信号的分离,而无须估计信号的概率密度和计算高阶累积量,减少了运算的复杂度并可用于杂系信号混合的盲分离问题;同时,算法利用仅具有一个全局最优解的凸代价函数,采用计算简单并具有较好数值表现的自适应共轭梯度算法进行迭代,获得了更快的收敛速度和更好的稳定性能。仿真结果表明,该算法与传统ICA算法相比,具有对时变系统更好的跟踪能力。

Aiming at the problem of poor tracking abilities of traditional independent component analysis （ICA）methods for time-varying channels,a conjugate gradient algorithm was proposed for blind source extraction of time-varying mixtures. The algorithm made effective use of the temporal structure difference among the source signals,and the sources with differ-ent power spectral density could be separated by the use of second-order statistics.Thus,there was no necessity to estimate the probability density of source signals or calculate their high-order statistics,in which way,the calculation complexity was decreased and the hybrid signals might also be separated.Meanwhile,the algorithm took advantage of the convex cost func-tion with only one global extreme point,and an adaptive conjugate gradient algorithm which was both easy and effective was used as the iteration algorithm.As a result,a faster convergence speed and a better stable ability were achieved.The simu-lation results indicate that,the proposed algorithm has better tracking ability for time-varying system than the traditional ICA ones.