Performance analysis of deficient-length RLS and EDS algorithms

Bei Xie, Tamal Bose, Zhongkai Zhang

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

2 Citations (Scopus)

Abstract

In practice, the length of the impulse response of the system to be identified is unknown and often infinite. When the system is modeled as an FIR filter, the length is usually shorter, and hence the name deficient-length filter. The learning rate, mean square error, and other properties of a deficient-length adaptive filter are different from that of a filter that is of sufficient length. In this paper, mean square error and convergence in the mean are analyzed for least square type deficient-length adaptive filters. In particular, we analyze Recursive Least Square (RLS) and Euclidean Direction Search (EDS) algorithms with deficient-length filters, and derive some mathematical properties. Simulation results agree with the theoretical analyses.

Original languageEnglish (US)
Title of host publication2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings
Pages115-120
Number of pages6
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009 - Marco Island, FL, United States
Duration: Jan 4 2009Jan 7 2009

Other

Other2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009
CountryUnited States
CityMarco Island, FL
Period1/4/091/7/09

Fingerprint

Adaptive filters
Mean square error
FIR filters
Impulse response

Keywords

  • Adaptive filtering
  • Convergence
  • EDS
  • RLS

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Xie, B., Bose, T., & Zhang, Z. (2009). Performance analysis of deficient-length RLS and EDS algorithms. In 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings (pp. 115-120). [4785906] https://doi.org/10.1109/DSP.2009.4785906

Performance analysis of deficient-length RLS and EDS algorithms. / Xie, Bei; Bose, Tamal; Zhang, Zhongkai.

2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings. 2009. p. 115-120 4785906.

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

Xie, B, Bose, T & Zhang, Z 2009, Performance analysis of deficient-length RLS and EDS algorithms. in 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings., 4785906, pp. 115-120, 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Marco Island, FL, United States, 1/4/09. https://doi.org/10.1109/DSP.2009.4785906
Xie B, Bose T, Zhang Z. Performance analysis of deficient-length RLS and EDS algorithms. In 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings. 2009. p. 115-120. 4785906 https://doi.org/10.1109/DSP.2009.4785906
Xie, Bei ; Bose, Tamal ; Zhang, Zhongkai. / Performance analysis of deficient-length RLS and EDS algorithms. 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings. 2009. pp. 115-120
@inproceedings{d1cc61e4a8834fae935bc697d91f7b5d,
title = "Performance analysis of deficient-length RLS and EDS algorithms",
abstract = "In practice, the length of the impulse response of the system to be identified is unknown and often infinite. When the system is modeled as an FIR filter, the length is usually shorter, and hence the name deficient-length filter. The learning rate, mean square error, and other properties of a deficient-length adaptive filter are different from that of a filter that is of sufficient length. In this paper, mean square error and convergence in the mean are analyzed for least square type deficient-length adaptive filters. In particular, we analyze Recursive Least Square (RLS) and Euclidean Direction Search (EDS) algorithms with deficient-length filters, and derive some mathematical properties. Simulation results agree with the theoretical analyses.",
keywords = "Adaptive filtering, Convergence, EDS, RLS",
author = "Bei Xie and Tamal Bose and Zhongkai Zhang",
year = "2009",
doi = "10.1109/DSP.2009.4785906",
language = "English (US)",
isbn = "9781424436774",
pages = "115--120",
booktitle = "2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings",

}

TY - GEN

T1 - Performance analysis of deficient-length RLS and EDS algorithms

AU - Xie, Bei

AU - Bose, Tamal

AU - Zhang, Zhongkai

PY - 2009

Y1 - 2009

N2 - In practice, the length of the impulse response of the system to be identified is unknown and often infinite. When the system is modeled as an FIR filter, the length is usually shorter, and hence the name deficient-length filter. The learning rate, mean square error, and other properties of a deficient-length adaptive filter are different from that of a filter that is of sufficient length. In this paper, mean square error and convergence in the mean are analyzed for least square type deficient-length adaptive filters. In particular, we analyze Recursive Least Square (RLS) and Euclidean Direction Search (EDS) algorithms with deficient-length filters, and derive some mathematical properties. Simulation results agree with the theoretical analyses.

AB - In practice, the length of the impulse response of the system to be identified is unknown and often infinite. When the system is modeled as an FIR filter, the length is usually shorter, and hence the name deficient-length filter. The learning rate, mean square error, and other properties of a deficient-length adaptive filter are different from that of a filter that is of sufficient length. In this paper, mean square error and convergence in the mean are analyzed for least square type deficient-length adaptive filters. In particular, we analyze Recursive Least Square (RLS) and Euclidean Direction Search (EDS) algorithms with deficient-length filters, and derive some mathematical properties. Simulation results agree with the theoretical analyses.

KW - Adaptive filtering

KW - Convergence

KW - EDS

KW - RLS

UR - http://www.scopus.com/inward/record.url?scp=63649093667&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=63649093667&partnerID=8YFLogxK

U2 - 10.1109/DSP.2009.4785906

DO - 10.1109/DSP.2009.4785906

M3 - Conference contribution

AN - SCOPUS:63649093667

SN - 9781424436774

SP - 115

EP - 120

BT - 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings

ER -