.png){kind=icon}
.png){kind=icon}
Return
Constrained squared sine derived adaptive algorithm: Performance and analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2023.109288.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A constrained squared sine derived adaptive (CSSDA) algorithm is proposed in this paper, which provides better steady-state behavior than existing algorithms in impulsive noise environments. The devised CSSDA works by constructing a squared sine function as the constrained cost function in solving the constrained adaptive filtering problem. Theoretical analysis of the CSSDA is presented and compared with the simulations. The simulation results show that the theoretical analysis agrees well with the simulations, helping to verify the effectiveness and correctness of the analysis. Also, the performance of the CSSDA is superior to the recent popular constraint adaptive algorithms for system identifications in a variety of environments with non-Gaussian impulsive noises.
Keywords:
Constrained adaptive filtering
Squared sine cost function
Steady-state mean square error
Impulsive noise