Stopping and restarting adaptive updates to recursive least-squares lattice adaptive filtering algorithms

Jake Gunther, Wang Song, Tamal Bose

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper reports several observations about stopping and restarting adaptive updates to recursive least-squares lattice (LSL) adaptive filtering algorithms. When updates are stopped, the adaptive filter becomes a fixed filter. Simulation examples demonstrate that large output error results from abruptly stopping or restarting adaptive updates. A remedy to the problem is to transition the adaptive updates to an off or on state gradually by driving the unknown system and the adaptive filter simultaneously to the all zero state. This is accomplished by setting the input signal to zero. The length (in number of samples) of the transition period is equal to the length of the adaptive filter. Simulation examples are given to illustrate the problem and the effectiveness of the proposed remedy.

Original languageEnglish (US)
Title of host publication2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006
Pages1-6
Number of pages6
DOIs
StatePublished - Dec 1 2006
Externally publishedYes
Event2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006 - Logan, Utah, United States
Duration: Jul 24 2006Jul 26 2006

Publication series

Name2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006

Other

Other2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006
CountryUnited States
CityLogan, Utah
Period7/24/067/26/06

ASJC Scopus subject areas

  • Computational Mathematics
  • Education

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    Gunther, J., Song, W., & Bose, T. (2006). Stopping and restarting adaptive updates to recursive least-squares lattice adaptive filtering algorithms. In 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006 (pp. 1-6). [4016753] (2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006). https://doi.org/10.1109/SMCALS.2006.250683