LMS iterative algorithms applied to the computation of TV ghost canceller parameters

Three iterative forms of the LMS learning algorithm were tested for the calculation of the coefficients of FIR filters used as TV ghost cancellers. These computational forms are: the stochastic gradient fixed-step (SGLMS) and a variable step (SLS-CD), algorithms, as well as the recursive modified Gr...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on consumer electronics 1994-08, Vol.40 (3), p.662-670
Main Authors: Yong, A., Markhauser, C.P.
Format: Article
Language:English
Subjects:
Finite impulse response filter
Iterative algorithms
Least squares approximation
Power generation
Sampling methods
Signal generators
Software algorithms
Stochastic processes
Testing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Three iterative forms of the LMS learning algorithm were tested for the calculation of the coefficients of FIR filters used as TV ghost cancellers. These computational forms are: the stochastic gradient fixed-step (SGLMS) and a variable step (SLS-CD), algorithms, as well as the recursive modified Gram-Schmidt RMGS algorithm. Because of the iterative nature of the selected algorithms, they are very convenient to be used in on-line LTF filter coefficient adaptations. This makes it possible to compute the coefficient values of the ghost canceller, when the sampling of the signal generates a huge amount of data, which is very hard to be handled with a PC. The aforementioned algorithms are written in a very powerful and flexible matrix oriented software, and all the tests were performed using a very flexible TV system simulator. During the tests, fast convergence of the ghost canceller coefficients to the theoretical values have been observed.< >
ISSN:0098-3063
1558-4127
DOI:10.1109/30.320855