### 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 language | English (US) |
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Title of host publication | 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings |

Pages | 115-120 |

Number of pages | 6 |

DOIs | |

State | Published - 2009 |

Externally published | Yes |

Event | 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009 - Marco Island, FL, United States Duration: Jan 4 2009 → Jan 7 2009 |

### Other

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

Country | United States |

City | Marco Island, FL |

Period | 1/4/09 → 1/7/09 |

### Fingerprint

### Keywords

- Adaptive filtering
- Convergence
- EDS
- RLS

### ASJC Scopus subject areas

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

### Cite this

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*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

}

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 -