摘要
介绍了噪声抵消的原理和从强噪声背景中自适应滤波提取有用信号的方法,并对最小均方(LMS,LeastMeanSquares)、归一化LMS(NLMS,NormalizedLeastMeanSquares)和递推最小二乘(RLS,RecursiveLeastSquares)三种基本自适应算法进行了对比研究。计算机模拟仿真结果表明,这几种算法都能通过有效抑制各种干扰来提高强噪声背景中的信号检测特性。相比之下,RLS算法具有良好的收敛性能,除收敛速度快于LMS算法和NLMS算法以及稳定性强外,而且具有更高的起始收敛速率;更小的权噪声,更大的抑噪能力。
The theory of noise canceling and the method for abstracting the desired signal from strong background noise were described by using adaptive filtering and the LMS algorithms 、 NLMS algorithms and RLS algorithms were compared. The results of computer simulation show that all of these adaptive algorithms can improve the detection of weak signal in strong background noise. In comparison, the RLS algorithm performance is much better than LMS algorithm and NLMS algorithm. Besides, the convergence speed is much faster and the behavior of the RLS filter coefficients is much more stable, and it has faster beginning convergence rate, lower misadjustment noise, and better robustness against noise and disturbance.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2006年第5期1178-1180,共3页
Journal of System Simulation
基金
云南省自然科学基金重点资助项目(2002C002Z)
云南省创新人才引培项目(2C02PY10)
关键词
自适应滤波
噪声抵消
算法
仿真
adaptive filtering
noise cancellation
algorithms
simulation
