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In this paper, we propose sparsity-aware data-selective adaptive filteringalgorithms with adjustable penalties. Prior work incorporates a penaltyfunction into the cost function used in the optimization that originates thealgorithms to improve their performance by exploiting sparsity. However, thestrength of the penalty function is controlled by a scalar that is often afixed parameter. In contrast to prior work, we develop a framework to derivealgorithms that automatically adjust the penalty function parameter and thestep size to achieve a better performance. Simulations for a systemidentification application show that the proposed algorithms outperform inconvergence speed existing sparsity-aware algorithms.

Study of Sparsity-Aware Set-Membership Adaptive Algorithms with Adjustable Penalties