摘要
本文研究了在输入输出观测数据均含有噪声的情况下如何有效地进行鲁棒自适应滤波的问题 .以总体均方误差 (TMSE)最小为准则 ,基于最速下降原理 ,通过对总体均方误差梯度进行修正 ,提出了一种鲁棒的总体均方最小自适应滤波算法 .通过与已有算法的对比分析表明 ,该算法能够有效地降低权向量的每步调整量对噪声的敏感程度 .仿真实验的结果进一步表明 ,该算法的鲁棒抗噪性能和稳态收敛精度明显地高于其它同类方法 ,而且可以使用较大的学习因子 ,在高噪声环境下仍然保持良好的收敛性 .
The robust adaptive filtering is researched when the input and output signal both corrupted by noise. On the basis of minimizing total mean square error (TMSE) and the steepest descent principle, by modifying the gradient of TMSE, a robust total least mean square (RTLMS) adaptive filter algorithm is proposed. The performance analysis compared with other algorithm demonstrates that the proposed algorithm can efficiently reduce the sensitivity of the weight adjusting-tap to noise. The simulation results show that the robust anti-noise performance and the stable convergence of the proposed algorithm are remarkably higher than other congener algorithms. And it can still keep nice convergence when a larger learning factor is used and the noise is strong.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2002年第7期1023-1026,共4页
Acta Electronica Sinica
关键词
鲁棒
自适应滤波
总体均方误差
总体均方最小
修正梯度
TMSE
信号处理
Algorithms
Computer simulation
Convergence of numerical methods
FIR filters
Gradient methods
Least squares approximations
Robustness (control systems)
Signal processing
Spurious signal noise
