V. Reddy

TL;DR: In this article, the fixed point error performance of the normalized ladder algorithm, for autoregressive system identification, assuming rounding arithmetic, was analyzed and a simplified theoretical expression for predicting the average bias in the estimated reflection coefficients at any stage was derived.

TL;DR: An alternative Gauss-Newton type recursive algorithm, which also used the second derivative matrix (or Hessian), which may be viewed as an approximate least squares algorithm and has faster convergence in the beginning, while its convergence rate close to the true parameters depends on the signal-to-noise ratio of the input signal.

TL;DR: An attempt is made to analyze the fixed point error performance of the normalized ladder algorithm for autoregressive system identification, assuming rounding arithmetic, and a simplified theoretical expression for predicting the average bias in the estimated reflection coefficients at any stage.