Variational Bayesian sparse adaptive filtering using a Gauss-Seidel recursive approach

In this work, we present a new sparse adaptive filtering algorithm following a variational Bayesian approach. First, sparsity is imposed by assigning Laplace priors to the filter parameters through a suitably defined hierarchical Bayesian model. Then, a variational Bayesian inference method is presented, which is appropriate for batch processing. In order to introduce adaptivity the Gauss-Seidel iterative scheme is properly embedded in our method. The proposed algorithm is fully automatic and is computationally efficient despite its Bayesian origin. Experimental results show that the algorithm converges to sparse solutions and exhibits superior estimation performance compared to related state-of-the-art schemes.