Abstract
In this work, we present the low-complexity variable forgetting factor (VFF) technique for the diffusion recursive least squares (RLS) algorithm. In particular, we adopt the VFF mechanism that rely on time-averages of the posteriori error signal and incorporate it into the diffusion RLS (DRLS) algorithm to yield the low-complexity correlated time-averaged VFF diffusion RLS (LCTVFF-DRLS) algorithm. We develop detailed analyses in terms of mean and mean square performance for the proposed algorithm, and derive mathematical expressions to compute the mean square deviation (MSD) and the excess mean square error (EMSE). The simulation results show that the proposed LCTVFF-DRLS algorithm outperforms the existing DRLS algorithm with the fixed forgetting factor, and demonstrate a good match between our proposed analytical expressions and simulated results.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
