一种新的变步长LMS自适应谱线增强算法

New variable step size LMS adaptive spectral-line enhancement algorithm

针对自适应谱线增强器(ALE)理想误差不为零的现象,提出了一种新的变步长LMS算法。该算法建立了步长因子与权系数变化之间的非线性关系,使得步长因子随权系数变化的减小而减小,直至为零,并引入步长向量来实时地逐个调整权系数的每个值,以进一步加快算法的收敛速度。它具有初始阶段和未跟踪上信号前步长自动增大而稳态时步长变小最后为零的特点,并以线性调频信号为例,验证了算法对滤除非平稳信号噪声的有效性,提高信号的信噪比,证实该算法优于传统算法。

Seeing that the ideal error of adaptive line enhancers （ALE） is not equal to zero, a novel variable step size LMS （least mean square） algorithm is presented. This algorithm builds a nonlinear relationship between the step size and the weighting coefficient, which makes the step size decrease with the change of weighting coefficients until the step size reduces to zero. In order to accelerate the convergence rate further, a stepvector is also introduced to adjust each weight vector value in real time. The step size of this algorithm increases at the beginning or before following up the signal automatically, and it will reduce to zero during the steady state. The LFM signal is taken as an example to verify the validity of the proposed algorithm in filtering the noise of non-stationary signals. The result shows this algorithm is better compared to the former.