针对此问题，提出一种基于高阶统计的快速分离算法，该算法可以有效地避免上述问题。
Herein, a new simpler and faster algorithm was introduced based on higher order statistics, which could overcome the problem of nonlinear function and step choice.
针对大背景噪声，本文提出采用快速ICA算法对碰摩与噪声信号进行分离。
The fast ICA algorithm is proposed to separate the rub-impact signal from the noise signal.
该算法基于恒模算法(CMA)，计算机仿真结果显示该算法收敛快速，性能稳定，能准确地完成多用户的分离。
This algorithm is based on constant module algorithm (CMA). The computer simulation results indicate that this algorithm have several advantages such as fast convergence, robustness and so on.
仿真结果表明，本文所提出的短波盲信道快速分析算法的分离精度和信道正确分离率都较高、抗噪声干扰能力强。
Simulation results show that the proposed blind channel algorithm is of high separation precision, high right separation rate and stronger anti-noise-interference ability.
仿真结果表明，本文所提出的短波盲信道快速分析算法的分离精度和信道正确分离率都较高、抗噪声干扰能力强。
Simulation results show that the proposed blind channel algorithm is of high separation precision, high right separation rate and stronger anti-noise-interference ability.
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