Decorrelation-based least mean square adaptive filtering algorithm based on theory of operators
一种基于算子理论的去相关LMS自适应滤波算法

To resolve the performance-worsening problem of the least mean square (LMS) algorithm caused by the correlation among the input signal vector sequence, this paper proposed a new decorrelation-based LMS algorithm based on theory of ope-rators. The algorithm extracted the innovation process by projecting the latest input signal vector into the null space of the linear space generated by all the previous input signal vectors orthogonally, and took the innovation process as the updating direction vector. Simulation results show that the new algorithm has characteristics as following: fast convergence, small output errors and insensitive to the signal to noise ratio. Further more, the filter adopting the new algorithm can obtain a satisfactory filtering effect and an improving operational efficiency as well by choosing a lower filter order.