Adaptive sparse Volterra system identification with l(0)-norm penalty

作者：Shi Kun^*; Shi Peng

来源：Signal Processing, 2011, 91(10): 2432-2436.

DOI：10.1016/j.sigpro.2011.04.028

• Volterra filter
• Sparsity
• Recursive least squares (RLS)
• l(0) norm

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 l(0)-norm constraint in the standard recursive least squares (RLS) cost function and an approximation of l(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.

• 出版日期2011-10