Online censoring based complex-valued adaptive filters

A class of complex-valued adaptive filtering algorithms is proposed, with the aim to reduce the cost of data processing in the complex domain. This is achieved by leveraging the advantages of the online censoring (OC) strategy and complex-valued adaptive signal processing (ASP). The proposed algorithms, namely the OC based complex-valued least mean square (OC-CLMS), OC based augmented CLMS (OCACLMS), OC based hybrid (OC-Hybrid), OC based complex-valued recursive least square (OC-CRLS), and OC based augmented CRL S (OC-ACRL S) algorithms, censor the less informative complex-valued data under certain rules, that is, they use only the most informative data for updating weight vectors. This is shown to considerably reduce the cost of data processing for processing both circular and noncircular complex valued signals. Moreover, to censor possible outliers in the complex domain, we also develop the robust versions of the proposed algorithms, called the ROC-CLMS, ROC-ACLMS, ROC-Hybrid, ROC-CRLS, and ROCACRLS algorithms. Simulation result s over both system identification and real-world prediction scenarios verify the attractive properties of the proposed OC strategy based complex-valued algorithms. Moreover, this study paves the way for using the proposed algorithms to process streaming big data in the complex domain. (c) 2022 Elsevier B.V. All rights reserved.