A Sparse Conjugate Gradient Adaptive Filter

• Published in: IEEE signal processing letters Vol. 27; pp. 1000 - 1004
• Main Authors: Lee, Ching-Hua, Rao, Bhaskar D., Garudadri, Harinath
• Format: Journal Article
• Language: English
• Published: United States IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive filters; Adaptive systems; Affine scaling; Algorithms; Computer simulation; conjugate gradient; Conjugates; Convergence; Correlation; Estimation; iterative reweighting; Markov processes; On-line systems; Optimization; Queuing theory; Regularization; Signal processing algorithms; Signal reconstruction; sparse adaptive filter; sparse signal recovery; Sparsity; sparse signal recovery; iterative reweighting; Affine scaling; conjugate gradient; sparse adaptive filter
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2020.3000459

In this letter, we propose a novel conjugate gradient (CG) adaptive filtering algorithm for online estimation of system responses that admit sparsity. Specifically, the Sparsity-promoting Conjugate Gradient (SCG) algorithm is developed based on iterative reweighting methods popular in the sparse signal recovery area. We propose an affine scaling transformation strategy within the reweighting framework, leading to an algorithm that allows the usage of a zero sparsity regularization coefficient. This enables SCG to leverage the sparsity of the system response if it already exists, while not compromising the optimization process. Simulation results show that SCG demonstrates improved convergence and steady-state properties over existing methods.