Fast communication: Adaptive sparse Volterra system identification with l 0 -norm penalty

This paper considers the adaptive identification of sparse Volterra systems. Based on the sparse nature of the Volterra model, a new cost function is proposed and a recursive method is derived for the estimation of Volterra kernel coefficients. Specifically, we exploit the system sparsity by incorporating an @?"[email protected]?norm constraint in the standard recursive least squares (RLS) cost function and an approximation of @?"[email protected]?norm is used to develop the recursive estimation method. Superior to the traditional RLS algorithm, our approach does not require a long data record to obtain a reliable estimation. Furthermore, compared to the existing methods, the proposed approach achieves comparable steady-state performance and lower computational complexity. The effectiveness of our method is illustrated by computer simulations.