An extended RLS type algorithm based on a non-linear function of the error

Volume 5, Issue 6, December 2020     |     PP. 136-140      |     PDF (486 K)    |     Pub. Date: December 2, 2020
DOI:    169 Downloads     12274 Views  

Author(s)

L.F. Coelho Amaral, Federal University of Maranhão, department of Mathematics, São Luís, Maranhão, Brazil
M. Vinicius Lopes, Federal University of Maranhão, department of Electrical Engineering, São Luís, Maranhão, Brazil
A. K. Barros, Federal University of Maranhão, department of Electrical Engineering, São Luís, Maranhão, Brazil

Abstract
Over the last few years, extended recursive and kernelized algorithms were one of the most promising in terms of tracking signals of state-space models in non-stationary environments. In this work, we intend to propose an EX-RLS (Extended Recursive Least Squares) algorithm based on a non-linear sum function of the error. The simulations were made in the problem by tracking a non-linear Rayleigh fading multipath channel. The results showed that the proposed algorithm exhibits a superior signal tracking capability than the kernelized extended recursive type versions.

Keywords
Recursive filter adaptive, ex-rnl algorithm, nonquadratic function, tracking, convergence rate

Cite this paper
L.F. Coelho Amaral, M. Vinicius Lopes, A. K. Barros, An extended RLS type algorithm based on a non-linear function of the error , SCIREA Journal of Electrical Engineering. Volume 5, Issue 6, December 2020 | PP. 136-140.

References

[ 1 ] A. H. Sayed. Fundamentals of Adaptive Filtering, HOBOKEN, NJ, USA: Wiley, 2003.
[ 2 ] L. F. C. Amaral, M. V. Lopes, A. K. Barros. A nonquadratic algorithm based on the extended recursive least squares algorithm, IEEE SIGNAL PROCESSING LETTERS, vol. 25, n0. 10, October 2018.5, Issue 10, 2018, pp. 1535-1539.
[ 3 ] W. Liu, I. Park, Y. Wang, and J. C. Príncipe. Extended kernel recursive least squares algorithm, IEEE Trans. Signal Process., vol 57, n0 10, pp. 3801-3814.
[ 4 ] E. Walach and B. Widdrow. The least mean fourth (LMF) adaptive algorithm and its family, IEEE Trans. Inf. Theory, vol. IT-30, n0 2, pp. 275-283.