In this paper, a perturbation expansion technique is introduced to decompose the tracking error of a general adaptive tracking algorithm in a linear regression model. This method results in a tracking error bound and tight approximate expressions for the moments of the tracking error. These expressions allow the evaluation, both qualitatively and quantitatively, of the impact of several factors on the tracking error performance.
['Rafik Aguech', 'Eric Moulines', 'Pierre Priouret']
On a Perturbation Approach for the Analysis of Stochastic Tracking Algorithms