Adaptive identification of sparse systems using the slim approach

In this paper, a novel time recursive implementation of the Sparse Learning via Iterative Minimization (SLIM) algorithm is proposed, in the context of adaptive system identification. The proposed scheme exhibits fast convergence and tracking ability at an affordable computational cost. Numerical simulations illustrate the achieved performance gain in comparison to other existing adaptive sparse system identification techniques.