Studies of optimal memory for discrete-time fir filters in state-space

Felipe Ramirez-Echeverria, Amadou Sarr, Oscar G. Ibarra-Manzano, Yuriy S. Shmaliy

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

5 Citations (Scopus)

Abstract

We address two efficient estimators of optimal memory for FIR filters in discrete-time state-space via the conditional mean square error and real measurement. In the latter case, the algorithm does not involve neither a reference nor the noise covariances, but requires a learning circle. Although a justification has been provided for the Kalman-like unbiased FIR filter, the estimators can be used universally. Testing by the two-state polynomial model has shown a very good correspondence with the predicted values.

Original languageEnglish
Title of host publication2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Pages349-352
Number of pages4
DOIs
Publication statusPublished - 2012
Event2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
Duration: Aug 5 2012Aug 8 2012

Other

Other2012 IEEE Statistical Signal Processing Workshop, SSP 2012
CountryUnited States
CityAnn Arbor, MI
Period8/5/128/8/12

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Keywords

  • FIR filtering
  • optimal memory
  • unbiased Kalman-like filter

ASJC Scopus subject areas

  • Signal Processing

Cite this

Ramirez-Echeverria, F., Sarr, A., Ibarra-Manzano, O. G., & Shmaliy, Y. S. (2012). Studies of optimal memory for discrete-time fir filters in state-space. In 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 (pp. 349-352). [6319701] https://doi.org/10.1109/SSP.2012.6319701