改进的变步长变换域最小均方算法

Modified Variable Step Size Transform Domain LMS Algorithm

变换域是一种在强相关信号输入时加快自适应算法收敛的方法,但仍然存在收敛速度的要求与稳态失调的要求相矛盾的问题。本文在变换域最小均方算法(transform domain LMS,TDLMS)的基础上提出了一种改进的变步长方案,其变步长因子受到误差自相关的控制,消除了不相关的观测噪声的影响。本文分别在平稳和非平稳状态下,对算法的收敛和稳态性能进行理论分析,并给出了最佳的算法参数。仿真设置相同的稳态误差,结果表明本文算法在平稳状态下比固定步长的算法提前1300点收敛,在非平稳状态下提前1400点收敛,且与文献中其他变步长的算法相比收敛速度均有提升。

Transform domain is an effective method to accelerate the convergence of adaptive algorithm with the highly correlated input,but there is still a contradiction between the requirement of convergence speed and steady-state misadjustment.A modified variable step size scheme based on the transform domain LMS is proposed in this paper.The variable step size factor is controlled by the error autocorrelation,and the influence of irrelevant observation noise is eliminated.In this paper,the convergence and steady-state performance of the proposed algorithm are theoretically analyzed under the stationary and non-stationary state,respectively,and the optimal algorithm parameters are given.The same steady-state error is set in the simulation,and the results show that the algorithm proposed in this paper converges 1300 points earlier than the algorithm with fixed step size under the stationary state,and 1400 points earlier under the non-stationary state,besides,the convergence speed is improved compared with other algorithms with variable step size.