Sparsity-aware adaptive filters based on ℓp-norm inspired soft-thresholding technique

We propose a novel sparsity-aware adaptive filtering algorithm based on iterative use of weighted soft-thresholding. The weights are determined based on a rough local approximation of the ℓ_p norm (0 <; p <; 1). The proposed algorithm operates the weighted soft-thresholding for enhancing the sparsity, following estimation error managements with the affine projection. The proposed weighting technique alleviates an extra bias of no benefit caused by shrinking dominant coefficients. The numerical examples demonstrate that the proposed weighting technique outperforms the existing one when the situation changes under the fixed parameter settings.