Title :
Optimum error nonlinearities for long adaptive filters
Author :
Al-Naffouri, Tareq Y. ; Sayed, Ali H.
Author_Institution :
Information Systems Lab, Electrical Engineering Department, Stanford University, CA 94305, USA
Abstract :
In this paper, we consider the class of adaptive filters with error nonlinearities. In particular, we derive an expression for the optimum nonlinearity that minimizes the steady-state error and attains the limit mandated by the Cramer-Rae bound of the underlying estimation process.
Keywords :
Gold; Noise; Steady-state;
Conference_Titel :
Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on
Conference_Location :
Orlando, FL, USA
Print_ISBN :
0-7803-7402-9
DOI :
10.1109/ICASSP.2002.5744885
