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There are two main families of algorithms that tackle the problem of sparse system identification: the proportionate family and the one that employs sparsity-promoting penalty functions. Recently, a new approach was proposed with the l0-IPAPA algorithm, which combines proportionate updates with sparsity-promoting penalties. This paper proposes some modifications to the l0-IPAPA algorithm in order...
Proportionate adaptive filters can improve the convergence speed for the identification of sparse systems as compared to their conventional counterparts. In this paper, the idea of proportionate adaptation is combined with the framework of set-membership filtering (SMF) in an attempt to derive novel computationally efficient algorithms. The resulting algorithms attain an attractive faster converge...
We propose two adaptive filtering algorithms that combine sparsity-promoting schemes with data-selection mechanisms. Sparsity is promoted via some well-known nonconvex approximations to the norm in order to increase convergence speed of the algorithms when dealing with sparse/compressible signals. These approximations circumvent some difficulties of working with the norm, thus allowing...
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