Using robust estimation algorithms for tracking explicit curves
TAREL ; IENG ; CHARBONNIER
Type de document
COMMUNICATION AVEC ACTES INTERNATIONAL (ACTI)
Langue
anglais
Auteur
TAREL ; IENG ; CHARBONNIER
Résumé / Abstract
The context of this work is lateral vehicle control using a camera as a sensor. A natural tool for controlling a vehicle is recursive filtering. The well-known Kalman filtering theory relies on Gaussian assumptions on both the state and measure random variables. However, image processing algorithms yield measurements that, most of the time, are far from Gaussian, as experimentally shown on real data in the authors' application. It's therefore necessary to make the approach more robust, leading the so-called robust Kalman filtering. In this paper, the authors review this approach from a very global point of view, adopting a constrained least squares approach, witch is very similar to the half-quadratic theory, and justifies to use of iterative reweighted least squares algorithms. A key issue in robust Kalman filtering is the choice of the prediction error covariance matrix. Unlike in the Gaussian case, its computation is not straight forward in the robust case, due to the nonlinearity of the involved expectation. We review the classical alternatives and propose new ones. A theoretical study of these approximations is out of the scope of this paper, however the authors do provide an experimental comparison on synthetic data perturbed with Cauchy-distributed noise. (document disponible en version électronique).
