抽象的

Performance Analysis of the Recursive Least Squares Algorithm

Writi Mitra, Subhojit Malik

Adaptive filter algorithm is a widely used method in communication systems, control systems, digital signal processing etc. This method helps to find out the unknown parameters iteratively by adjusting the filter parameters. There are many efficient adaptive filter algorithms. But among them, the basic algorithms are: Least Mean Square (LMS) and Recursive Least Square (RLS) Algorithms. The LMS algorithm is based on gradient optimization and the RLS algorithm is based on direct form FIR and lattice realization. The RLS algorithm is popular because of its fast convergence although the LMS algorithm is very simple to implement. There are modified LMS algorithms and they are: Leaky Least Mean Square (LLMS) Algorithm and Normalized Least Mean Square (NLMS) Algorithm. „Step size�?? is an important parameter which is used to implement any of these LMS algorithms. In case of RLS algorithm, one term „forgetting factor�?? plays an important role in times of implementing any system.