Ozince, NazimMenguc, Engin CemalEmlek, Alper2024-11-072024-11-0720250165-16841872-7557https://doi.org/10.1016/j.sigpro.2024.109637https://hdl.handle.net/11480/14675Complex-valued least mean kurtosis (CLMK) algorithm and its augmented version (ACLMK) have recently become popular as workhorse tools in the processing of complex-valued signals due to their superior performances. Unfortunately, they are not yet suitable for sparse system identification problems. In this paper, combining the well-known sparsity-promoting strategies into the cost function based on the negated kurtosis of the error signal, we introduce a suit of sparsity-aware CLMK algorithms, named /0 0-norm CLMK (/0-CLMK), / 0-CLMK), / 0-ACLMK, zero-attraction CLMK (ZA-CLMK), ZA-ACLMK, reweighted ZA-CLMK (RZA-CLMK), and RZA-ACLMK. Simulation results on synthetic and real-world sparse system identification scenarios in the complex domain show that the proposed algorithms outperform the existing sparsity-aware algorithms in terms of convergence rate, tracking, and steady-state error.eninfo:eu-repo/semantics/closedAccessComplex-valued least mean kurtosisComplex-valued signalsSparse system identificationAugmented statisticsArticle22610.1016/j.sigpro.2024.1096372-s2.0-85200759681Q1WOS:001292170300001N/A