摘要
噪声条件下多个直扩信号混合情况下的参数估计是传统算法所无法处理的,为此提出将去噪盲分离算法应用于此问题,达到噪声消除和使多个扩频信号相互分离的目的。首先回顾了在低信噪比条件下估计单个直扩信号参数的循环谱估计技术,并且说明了盲分离算法在估计多个混合直扩信号参数的可行性。然后给出了含噪盲分离的基本模型和一种有效算法移偏快速独立分量分析(fast independent component analysis,FASTICA)。接着引出了一个算法框架——去噪盲分离,证明了经典独立成分分析(independent component analysis,ICA)算法可以统一到这个框架中。仿真结果表明了算法的有效性和实用性。
As the parameters estimation of multiple direct sequence spread spectrum（DSSS） signals in noise condition is difficult for traditional methods,the denoising source separation（DSS） method is introduced to achieve the goal of mixed-signal separation and noise reduction.First parameters estimation of the DSSS signal based on cyclic spectrum techniques is reviewed under the conditions of low SNR.And the blind source separation algorithm is explained to be feasible in mixed DSSS signal parameters estimation.Then the basic model of blind source separation and an effective algorithm called bias-removal fast independent component analysis（FASTICA） are given.An algorithm framework for blind sources separation—DSS is derived,and that classical ICA algorithms can be unified into this framework is proven.The simulation results show the effectiveness and practicability of the algorithm.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2011年第8期1722-1726,共5页
Systems Engineering and Electronics
基金
国防"十一五"预研项目资助课题
关键词
去噪盲分离
直扩信号
循环谱估计
移偏快速独立分量分析
denoising source separation（DSS）
direct sequence spread spectrum signal
cyclic spectrum estimation
bias-removal fast independent component analysis（FASTICA）
