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Sensors 2016, 16(5), 729; doi:10.3390/s16050729

Systematic Error Modeling and Bias Estimation

Robotics and Embedded Systems, Technische Universität München, 80333 München, Germany
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Academic Editor: Xue-Bo Jin
Received: 24 March 2016 / Revised: 6 May 2016 / Accepted: 16 May 2016 / Published: 19 May 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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Abstract

This paper analyzes the statistic properties of the systematic error in terms of range and bearing during the transformation process. Furthermore, we rely on a weighted nonlinear least square method to calculate the biases based on the proposed models. The results show the high performance of the proposed approach for error modeling and bias estimation. View Full-Text
Keywords: systematic error; bias; least square method systematic error; bias; least square method
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, F.; Knoll, A. Systematic Error Modeling and Bias Estimation. Sensors 2016, 16, 729.

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