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Transient Performance Analysis of the $\ell_1$-RLS

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arxiv 2109.06749 v2 pith:UFUTEMOU submitted 2021-09-14 eess.SP

Wei Gao , Jie Chen , Cédric Richard , Wentao Shi , Qunfei Zhang This is my paper
classification eess.SP
keywords performancetransientbehaviormodelsaccuracyalgorithmanalysisanalytical
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abstract

The recursive least-squares algorithm with $\ell_1$-norm regularization ($\ell_1$-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 $\ell_1$-RLS in the mean and mean-square sense. Simulation results illustrate the accuracy of these models.

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