@inproceedings{bac56bf914b24153bafd4d170a0a4806,
title = "Measuring the robustness of sequential methods",
abstract = "Whenever we apply methods for processing data, we make a number of model assumptions. In reality, these assumptions are not always correct. Robust methods can withstand model inaccuracies, that is, despite some incorrect assumptions they can still produce good results. We often want to know how robust employed methods are. To that end we need to have a yardstick for measuring robustness. In this paper, we propose an approach for constructing such metrics for sequential methods. These metrics are derived from the Kolmogorov-Smirnov distance between the cumulative distribution functions of the actual observations and the ones based on the assumed model. The use of the proposed metrics is demonstrated with simulation examples.",
keywords = "Extended Kalman filtering, Particle filtering, Robustness",
author = "Djuri{\'c}, \{Petar M.\} and Bugallo, \{M{\'o}nica F.\} and Pau Closas and Joaqu{\'i}n M{\'i}guez",
year = "2009",
doi = "10.1109/CAMSAP.2009.5413275",
language = "English",
isbn = "9781424451807",
series = "CAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing",
pages = "29--32",
booktitle = "CAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing",
note = "2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009 ; Conference date: 13-12-2009 Through 16-12-2009",
}