## Abstract

We consider the convergence properties of the forgetting factor RLS algorithm in a stationary data environment. We study the dependence of the speed of convergence of RLS with respect to the initialization of the input sample covariance matrix and with respect to the observation noise level. By obtaining estimates of the settling time we show that RLS, in a high SNR environment, when initialized with a matrix of small norm, has a very fast convergence. Convergence speed decreases as we increase the norm of the initialization matrix. In a medium SNR environment the optimum convergence speed of the algorithm is reduced, but RLS becomes more insensitive to initialization. Finally in a low SNR environment it is preferable to start the algorithm with a matrix of large norm.

Original language | English (US) |
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Pages | 370-373 |

Number of pages | 4 |

State | Published - 1996 |

Externally published | Yes |

Event | Proceedings of the 1996 7th IEEE Digital Signal Processing Workshop - Loen, Norway Duration: Sep 1 1996 → Sep 4 1996 |

### Other

Other | Proceedings of the 1996 7th IEEE Digital Signal Processing Workshop |
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City | Loen, Norway |

Period | 9/1/96 → 9/4/96 |

## All Science Journal Classification (ASJC) codes

- Signal Processing
- Electrical and Electronic Engineering