A parameter-acelerating algorithm.

Author(s)

Pablo Emilio Jojoa Gómez

• Document type: Doctoral Thesis
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Escola Politécnica (EP/BC)
• Defense date: 2003-10-30

Examining board members

Vitor Heloiz Nascimento; Jose Carlos Moreira Bermudez; Ricardo Merched; Maria das Dores dos Santos Miranda

• Advisor: Vitor Heloiz Nascimento

Abstract

In the digital signal processing field and specially in adaptive filtering, there is a constant search for algorithms both simple and with good performance. This work presents new discrete-time algorithms called accelerating algorithms (APCM and ARg), obtained through the discretization of a continuous-time algorithm that uses the second derivate (acceleration) to adjust the parameter estimates. We provide theoretical analyses for both algorithms, finding expressions for the mean and mean-square errors in the parameter estimates. In addition, we compare the performance of the accelerating algorithms with LMS and NLMS. The analysis of the APCM algorithm showed that its performance is inferior to that of the LMS algorithm. On the other hand, the ARg algorithm presented good performance when compared in terms of misadjustment and tracking with the NLMS algorithm, showing a better compromise between convergence speed and variance of the estimates. This better performance was proven by theoretical analyses, by simulations and through the application of this algorithm to the equalization of a time-variant channel. (AU)