Adaptive sparse Volterra system identification with ?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 ?0?norm ? 0 ? norm constraint in the standard recursive least squares (RLS) cost function and an approximation of ?0?norm ? 0 ? 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.
sparsity recursive least squares (rls) volterra filter ?0 ? 0 norm ?0 ? 0 ?0 ? 0 ? 0