Improved sparsity‐aware NSAF‐SF adaptive algorithm

Abstract

Normalised subband adaptive filtering algorithms have attracted attention due to their ability to present faster convergence in the case of coloured excitation data. The NSAF-SF scheme is an example of a state-of-the-art subband adaptive algorithm that demands a low computational burden. This Letter proposes a deterministic local optimisation approach with affine constraints whose result leads to an enhanced NSAF-SF updating mechanism. One of these constraints consists of a projection into a hyperplane derived from a relaxed ℓ 1-norm restriction, which provides a sparsity-promoting scheme. Such a constraint incorporates prior information into the (originally sparsity-agnostic) NSAF-SF. Such prior information concerns the energy concentration of the ideal transfer function that the adaptive filter intends to identify. Furthermore, the advanced optimisation problem is modified in order to make the advanced adaptive filter robust against impulsive noise. The simulations present a performance improvement for both transient and steady-state regions.