Transient Performance Analysis of the l1-RLS

The recursive least-squares algorithm with l(1)-norm regularization (l(1) -RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identification of sparse systems. Nevertheless few works have studied its stochastic behavior, in particular its transient performance. In this letter, we derive analytical models of the transient behavior of the l(1)-RLS in the mean and mean-square error sense. Simulation results illustrate the accuracy of these models.

Automated summary

This letter investigates the performance of the recursive least-squares algorithm with l(1)-norm regularization in identifying sparse systems and derives analytical models for its transient behavior. Simulation results confirm the accuracy of these models.