<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, two robust fusion algorithms for a linear system with observation uncertainty are proposed. The first algorithm is based on the classical median function and the second one uses relative distances between local estimates and their median value. In the view of estimation accuracy, the proposed fusion algorithms can be robust against uncertainty measurements since median can avoid extremely big or small values. This fact is verified from comparative analysis using numerical examples.</p>
</abstract>

In this paper, two robust fusion algorithms for a linear system with observation uncertainty are proposed. The first algorithm is based on the classical median function and the second one uses relative distances between local estimates and their median value. In the view of estimation accuracy, the proposed fusion algorithms can be robust against uncertainty measurements since median can avoid extremely big or small values. This fact is verified from comparative analysis using numerical examples.

Robust Fusion Algorithms for Linear Dynamic System with Uncertainty

Gwangju Institute of Science and Technology

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Robust Fusion Algorithms for Linear Dynamic System with Uncertainty"}

In this paper, two robust fusion algorithms for a linear system with observation uncertainty are proposed. The first algorithm is based on the classical median...

Robust Fusion Algorithms for Linear Dynamic System with Uncertainty

Publié en ligne: 12 déc. 2017

Pages: 146 - 155

DOI: https://doi.org/10.21307/ijssis-2017-386

© 2010 Inhea Beak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.